Method and system for collecting and processing bioelectrical signals

ABSTRACT

A method and system for detecting bioelectrical signals from a user, including establishing bioelectrical contact between a user and one or more sensors of a biomonitoring neuroheadset; collecting one or more reference signal datasets; collecting, at the one or more sensors, one or more bioelectrical signal datasets referenced to a combined reference signal dataset; and extracting one or more bioparameters from the one or more bioelectrical signal datasets.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/390,881, filed 22 Apr. 2019, which is a continuation-in-part of U.S.application Ser. No. 15/209,582, filed 13 Jul. 2016, which claims thebenefit of U.S. Provisional Application Ser. No. 62/201,256, filed 5Aug. 2015, which are each incorporated in their entirety herein by thisreference. This application claims the benefit of U.S. ProvisionalApplication Ser. No. 62/660,853 filed 20 Apr. 2018, which isincorporated herein in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the field of digital signalcollection and processing, and more specifically to a new and usefulmethod and system for collecting, processing, and analyzingbioelectrical signals.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts data flows of an embodiment of a method for detectingbioelectrical signals of a user.

FIG. 2 depicts a flowchart of an embodiment of the method

FIG. 3 depicts a schematic representation of an example of controlling auser device.

FIGS. 4A-4B depict variations of an embodiment of a system for detectingbioelectrical signals of a user.

FIGS. 5A-5C depict graphical representations of user anatomical regionsand variations of an embodiment of the system.

FIG. 6 depicts a graphical representation of a variation of anembodiment of the system.

FIG. 7 depicts a block diagram of a variation of an embodiment of thesystem.

FIG. 8 depicts a block diagram of a variation of an embodiment of thesystem.

FIG. 9 depicts a block diagram of a variation of an embodiment of thesystem.

FIG. 10 depicts a block diagram of a variation of an embodiment of thesystem.

FIG. 11 depicts a block diagram of a variation of an embodiment of thesystem including contact quality monitoring.

FIG. 12 depicts a graphical representation of a variation of anembodiment of the system.

FIGS. 13A-13C depict perspective, side, and top views of a variation ofan embodiment of the system.

FIG. 14 depicts a flow chart of an embodiment of a portion of the methodimplemented by a specific example embodiment of the system.

FIG. 15 is a schematic representation of an example of the method.

FIG. 16 is a specific example of extracting heart features from an EEGsignal.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview.

As shown in FIGS. 1-2 , an embodiment of a method 100 for detectingbioelectrical signals from a user, includes: establishing bioelectricalcontact between a user and a sensor of a biomonitoring neuroheadsetS110; sampling, at the sensor, a bioelectrical signal dataset S120;collecting one or more reference signal datasets S124; generating areference signal dataset from the one or more reference signal datasetsS130; and extracting a bioparameter from the bioelectrical dataset incombination with the reference signal dataset S140. In variants, S124can include collecting two or more reference signal datasets, and S130can include generating a combined reference signal dataset (e.g.,averaged reference signal dataset) from two or more reference signaldatasets, wherein the bioparameter can be extracted from thebioelectrical dataset in combination with the averaged reference signaldataset. The method 100 can additionally or alternatively include:determining auxiliary data at the biomonitoring headset S135;transmitting a dataset to a user and/or user device S150; generating ananalysis of a dataset S160; operating a device (e.g., the biomonitoringheadset, a third party device, etc.) S110; and, determining a stimulusS180. While variations of the method can be performed exclusively by abiomonitoring neuroheadset, other variations of the method can involveperformance of portions of the method by any component of a system 200,including one or more of a remote server, a local processing system, auser device (e.g., a smartphone, a laptop, a tablet, a desktop, asmartwatch, etc.), a third party device, and/or any other suitablecomponent.

In variations, the method 100 functions to collect, process, and analyzebioelectrical signals for monitoring psychological and/or physiologicalstatus of a user. The method 100 can additionally or alternativelyfunction to leverage analyzed bioelectrical signals to specify controlinstructions for a user device (e.g., a smartphone, a biomonitoringneuroheadset, etc.), to generate (e.g., extract, create, process datainto, etc.) bioparameters (e.g., a cognitive state metric describingemotional status, cardiovascular parameters, etc.) describing a user,and/or to provide stimulus (e.g., audio therapy) at an output 260 (e.g.,speaker) of the biomonitoring neuroheadset. The method 100 is preferablyperformed with an embodiment, variation, or example of the system 200(e.g., described in Section 4), but can alternatively be performed withany other suitable system and/or component.

As shown in FIGS. 13A-C, an embodiment of a system 200 for detectingbioelectrical signals from a user includes: two or more bioelectricalsignal sensors 210 configured to collect bioelectrical signal data fromthe user; a noise reduction subsystem 220 including two or morereference sensors configured to collect reference signal datacontemporaneously with the collection of bioelectrical signal data; anaveraging module 230 coupled to the two or more reference sensorsconfigured to generate an averaged reference signal (e.g., averagedreference signal data); a wearable support frame 240 worn at a headregion of the user, the wearable support frame 240 supporting andphysically connecting the two or more bioelectrical sensors 210 and thetwo or more reference sensors; and an electronics subsystem 250including a processing module 252 configured to extract a bioparameterat least in part from processing the bioelectrical signal data with theaveraged reference signal data, the electronics subsystem 250electronically connected to the one or more bioelectrical signal sensors210, the noise reduction subsystem 220, and the averaging module 230.

The system 200 functions to collect and process multiple types of data(e.g., electroencephalogram data, electrocardiogram data, audio/visualsignal data, bioelectrical data, haptic feedback data, etc.) formonitoring psychological and/or physiological status of a user. Thesystem 200 can additionally or alternatively function to ensure a highlevel of contact quality between sensors of the biomonitoringneuroheadset and the user, in order to accurately collect and analyzedifferent types of data. The system 200 can perform any portions of themethod 100 (e.g., described in Section 3), but the system 200 canadditionally or alternatively perform any other suitable operations.

2. Benefits.

In specific examples, the method 100 and/or system 200 can conferseveral benefits over conventional methodologies for collecting andprocessing bioelectrical signals (e.g., with auxiliary signals such asaudio signals, multiple bioelectrical signal streams, etc.). Traditionalapproaches can face limitations from inability to contemporaneouslymeasure multiple bioelectrical signals while preventing cross-talk andsufficiently eliminating associated noise. However, in specificexamples, the method 100 and/or system 200 can perform one or more ofthe following:

First, the technology can leverage a biomonitoring neuroheadset forcontinuously monitoring bioelectrical signals of users for use in aplethora of time-dependent applications such as monitoring health statusand neuromarketing. In an example of determining bioparameters, thetechnology can analyze electroencephalogram (EEG) signals and user inputin order to determine both psychological status (e.g., generating acognitive state metric corresponding to a user's emotional state at agiven time period) and physiological status (e.g., determiningcardiovascular bioparameters for a time period from EEG data collectedproximal an ear region of the user). In an example of consumer research,neuromarketing, and/or neuromarketing or consumer insights, thetechnology can collect EEG signals and audio commentary from a user asthe user is exposed to different types of media (e.g., advertisements,television, movies, video games, etc.) in order to determine howdifferent aspects of media affect a user's emotional state.

Second, the technology can simultaneously collect both bioelectricalsignals (e.g., EEG, electrocardiogram, electromyography,electrooculography, etc.) and auxiliary signals while preventingcross-talk and reducing noise associated with the signals in real-time.As such, the technology can generate combined bioelectrical signal andauxiliary signal processed datasets that are accurate and primed forsubsequent transmission (e.g., to a user device, to a remote processingmodule, etc.) and analysis (e.g., for evaluating cognitive state).

Third, the technology can simultaneously collect bioelectrical signalsfrom multiple body regions of the user and perform averaging and otherprocessing operations on the simultaneously collected signals to improvesystem performance (e.g., reduce noise, reduce EEG event detectionthresholds, etc.). As such, the technology can generate bioelectricalsignal datasets (e.g., aggregated bioelectrical datasets) from one ormore biosensors that are accurate and primed for subsequent transmission(e.g., to a user device, to a remote processing module, etc.) andanalysis (e.g., for evaluating cognitive state). In one variation, themethod generates a more accurate representation of the common mode noiseand reduces the proximal signal component (e.g., typically present inmeasurements by reference sensors located proximal the biosensor oractive electrode) by averaging the raw (bioelectrical) reference signalssampled at two or more physical locations on the user proximal (e.g.,within several millimeters or centimeters of) the biosensor location(s).In a second variation, the method generates a more accuraterepresentation of the common mode noise, even if the reference sensors(e.g., CMS electrodes, DRL electrodes) are arranged distal (e.g.,separated by more than several millimeters or centimeters of) thebiosensors. In an example, the use of multiple DRL locations distributesthe feedback and also halves the contact impedance for the feedbacksystem. The biosensors can include EEG sensors, active sensors,biopotential sensors, biosignal sensors, sensors that can sample anysubset of the channels of a 10:20 EEG system, and/or any other suitableset of operational sensors. This can create a virtual resultantreference signal (e.g., referential montage) that is effectively mid-waybetween the two ear reference signals (e.g., in signal space).Furthermore, any independent EEG contribution from reference signalscollected at each ear flap can be significantly reduced by the averagingprocess. Because the resultant reference signal averages out theproximal signal component present in the reference signals (e.g.,minimizes the local effects of to-be-measured brain signals on areference signal sampled near the biosensor), the resultant bioparametervalues (e.g., EEG measurements), which are determined based on thepotential difference between the bioelectrical signals (e.g., EEGsignals) and the resultant reference signal, can have higher signalfidelity, while maintaining the common mode rejection.

In an example, the reference signal can be determined from the averagevalue of signals (e.g., bioelectrical signals, biosignals) sampled fromeach of two ear locations (e.g., reference signals derived from theright and left ear flaps, wherein sensors are so located to collectbioelectrical signals) such that the reference signal contains averagedportions from both ear locations. The averaging process maintains thosebiosignal components common to both sensor locations (e.g., the “commonmode signal” or background body potential), and the differentialmeasurement of the biosignal at each ear canal location maintains asimilar common mode rejection (e.g., substantially improving the commonmode signal by removing or reducing non-common components, such as EEGand local muscle signals, which are present in a signal measured from asingle location) as a reference sensor placed more closely to one or theother ear location. Simultaneously, the location-specific (e.g., EEG)signals from the two ear locations are measured relative to a referencelevel (e.g., the resultant reference signal), which contains adiminished contribution of the local signal (e.g., as opposed to thereference electrode proximal the respective ear location) due to theaveraged referencing process. Measuring the location-specific signalsrelative to the resultant reference signal can result in improved signalfidelity versus a proximally-located reference sensor, which can canceldesired brain-derived signals due to the closely-collocated sensors.

Fourth, the technology can optionally include speakers (e.g., embeddedin an ear bud with an EEG sensor), which can be used for emitting audio(e.g., audio therapies generated based on evaluated cognitive state of auser, music for recreational purposes, music for therapeutic purposes,etc.). A user's response (e.g., EEG signal response, audio signalresponse, evoked potential response etc.) to emitted audio samples canbe continuously monitored using the biomonitoring neuroheadset providingthe audio sample. In some examples, the user's cognitive state can beused as feedback to the audio source to adjust the emitted audio (e.g.,change a song due to a user preference expressed in the cognitive state,such as a negative valence associated with the current song).

Fifth, as the technology can collect bioelectrical signals and audiosignals contemporaneously in real-time, generated analyses (e.g.,generated bioparameters, therapies, etc.) of collected data can bepresented and/or promoted to a user in real-time (e.g., during the timeperiod in which the data was collected). As such, the technology canprovide real-time and/or retrospective (e.g., leveraging collected datastored at remote server) analyses. The technology can optionally collectcontextual data and/or supplemental data (e.g., in addition to the EEGdata), wherein the bioparameter can be determined from all or a subsetof the aforementioned data (e.g., using sensor fusion, trained neuralnetworks, etc.).

The technology can, however, provide any other suitable benefit(s) inthe context of collecting bioelectrical signals and/or auxiliary signalsfor evaluating user status.

3. Method.

As shown in FIGS. 1-2 , an embodiment of a method 100 for detectingbioelectrical signals from a user can include: establishingbioelectrical contact between a user and a sensor of a biomonitoringneuroheadset S110; sampling, at the sensor, a bioelectrical signaldataset S120; collecting one or more reference signal datasets S124;generating an averaged reference signal dataset from the two or morereference signal datasets S130; and extracting a bioparameter from thebioelectrical dataset in combination with the averaged reference signaldataset S140.

In some variations, the method 100 can additionally or alternativelyinclude: determining auxiliary data at the biomonitoring headset S135;transmitting a dataset to a user S150; generating an analysis of adataset S160; operating a device (e.g., the biomonitoring headset, athird party device, etc.) S110; and, determining a stimulus S180.

In relation to the method 100, the two or more bioelectrical datasetsare preferably combined with the averaged reference signal dataset inthe analog domain, and the bioparameter is extracted from thecombination (e.g., subsequent to amplification of the combination,differential amplification of the combination, amplification of thedifferential between the bioelectrical signal datasets and the averagedreference signal datasets, etc.) in the digital domain (e.g., via adigital computational process). In alternative variations, the two ormore bioelectrical datasets are transformed into the digital domainprior to extraction of the bioparameter (e.g., in combination with theaveraged reference signal dataset, which can be in the digital or analogdomain). However, in additional or alternative variations, any suitableBlock of the method 100 can be performed in the analog or digitaldomain, using any suitable components for signal processing in theanalog or digital domains, respectively.

3.1 Establishing Bioelectrical Contact.

As shown in FIG. 2 , Block S110 recites: establishing bioelectricalcontact between a user and two or more sensors of a biomonitoringneuroheadset, which functions to facilitate a bioelectrical interfacebetween an individual and a biosignal detector. Establishingbioelectrical contact S110 is preferably between sensors of abiomonitoring neuroheadset and a human, but can additionally oralternatively be with a biomonitoring neuroheadset and any othersuitable organism (e.g., a pet, an animal, etc.). One or morebioelectrical sensors of the biomonitoring neuroheadset preferablyinclude one or more EEG sensors and one or more reference sensors (e.g.,reference electrodes, common mode sense (CMS) biopotential electrodes,driven right leg (DRL) passive electrodes, etc.). Alternatively, thebiomonitoring neuroheadset can omit specific reference sensors andself-reference the bioelectrical potential measurement (e.g., against anarbitrary reference value, a predetermined reference value, anelectrical potential value detected at another EEG sensor, etc.) and/orotherwise suitably obtain a differential potential measurement. However,the biomonitoring neuroheadset can additionally or alternatively includeany bioelectrical signal sensors configured to detect any one or moreof: electrooculography (EOG) signals, electromyography (EMG) signals,electrocardiography (ECG) signals, galvanic skin response (GSR) signals,magnetoencephalogram (MEG) signals, and/or any other suitable signal.

Relating to Block S110, bioelectrical contact is preferably establishedthrough sensors arranged at a particular location or region of the user(e.g., head region, torso region, etc.). The sensors of thebiomonitoring headset are preferably arranged at contralateral regionsof a head region of the user in order to facilitate biosignal detectionat opposing sides of the head region, but can be otherwise suitablyarranged. For example, Block S110 can include establishing bioelectricalcontact between a first subregion of an ear region of the user and anEEG sensor of a biomonitoring neuroheadset. In a specific example, thefirst subregion of the ear region (e.g., an ear region of a left ear)can include an ear canal (e.g., a left ear canal) of the user. BlockS110 can also include establishing bioelectrical contact between a firstcontralateral subregion of a contralateral ear region (e.g., an earregion of a right ear) of the user and a second EEG sensor of thebiomonitoring neuroheadset, where the first contralateral subregion caninclude a contralateral ear canal (e.g., a right ear canal) of the user.

In a first variation of Block S110 where the biomonitoring neuroheadsetincludes one or more common mode sensors, Block S110 can additionally oralternatively include establishing bioelectrical contact between asecond subregion of the ear region of the user and a reference sensor(e.g., a common mode sensor) of a noise reduction subsystem of thebiomonitoring neuroheadset S112. In a specific example of the variation,the second ear subregion is proximal the first subregion (e.g., withinseveral millimeters, several centimeters, several inches, etc.), and theEEG sensor is proximal the reference sensor. In another specificexample, the second ear subregion is distal the first subregion (e.g.,several centimeters apart, several inches apart, etc.), and the EEGsensor is distal the reference sensor. In another specific example,Block S112 can include establishing bioelectrical contact between asecond contralateral subregion of the contralateral ear region of theuser and a second common mode sensor of a noise reduction subsystem ofthe biomonitoring neuroheadset. In this specific example, the secondsubregion can include an ear subregion proximal a mastoid process of atemporal bone of the user, and where the second contralateral subregioncan include a contralateral ear subregion proximal a contralateralmastoid process of a contralateral temporal bone of the user. In thisspecific example, the first contralateral subregion is proximal thesecond contralateral subregion, and the second EEG sensor is proximalthe second common mode sensor. In an illustrative example of thespecific example, the system can include a left and right EEG sensor,and a right CMS sensor. In a second illustrative example, theillustrative example above can additionally include a second averagedCMS sensor.

In a second variation of Block S110 where the biomonitoring neuroheadsetincludes one or more driven right leg (DRL) sensors of a driven rightleg module, Block S110 can include establishing bioelectrical contactbetween a third subregion of the ear region of the user and a DRLelectrode of a DRL module of the noise reduction subsystem S114. Thethird subregion is preferably at an ear region (e.g., proximal a mastoidprocess of a temporal bone of the user), but can alternatively be at anysuitable anatomical position of the user. This variation can be used incombination with, or independently from, the second variation.

In one example, the biomonitoring neuroheadset includes two or more DRLelectrodes arranged in two locations on the user (e.g., a third andfourth ear subregion), in addition to two or more CMS sensors arrangedon the user. In a specific example, the headset includes at least afirst and second DRL electrode arranged proximal (e.g., within severalmillimeters, centimeters, etc.) another reference sensor, such as a CMSsensor (e.g., wherein the first and second DRL electrodes are arrangedproximal a first and second reference sensor, respectively, but canalternatively be arranged proximal the same reference sensor). In thisspecific example, the reference sensors (that the DRL electrodes arearranged proximal to) are preferably the reference sensors outputtingthe reference signals that are subsequently averaged, but canalternatively be other reference signals. This can function to generatea virtual reference similar to a linked-ear reference, which can canceltrue common mode signals and/or resolve imbalanced compensation problemsresulting from collocated DRL and CMS electrodes. This can also providesome redundancy in the feedback path, which can improve the chance thatat least one DRL electrode will be in good contact with the user.

In variations of Block S110, establishing bioelectrical contact caninclude any elements analogous to those disclosed in U.S. patentapplication Ser. No. 13/903,861 filed 28 May 2013, U.S. patentapplication Ser. No. 14/447,326 filed 30 Jul. 2014, and U.S. patentapplication Ser. No. 15/058,622 filed 2 Mar. 2016, which are herebyincorporated in their entirety by this reference. However, establishingbioelectrical contact between body regions of a user and different typesof sensors can be performed in any suitable manner.

3.2 Collecting a Bioelectrical Signal Dataset.

As shown in FIG. 2 , Block S120 recites: collecting, at the one or moresensors, one or more bioelectrical signal datasets, which functions tocollect data indicative of a psychological and/or physiological state ofa user. The collected dataset(s) preferably include bioelectrical signaldata collected at one or more sensors in bioelectrical contact with theuser as described in relation to Block S110. For example, Block S120 caninclude collecting one or more EEG, EOG, EMG, ECG, GSR, and/or MEGsignal datasets at corresponding bioelectrical sensors. Additionally oralternatively, any suitable dataset (e.g., supplemental data, etc.) canbe collected at a user device. Further, any number, size and/or sizes ofdatasets can be collected.

In relation to Block S120, collecting one or more bioelectrical signaldatasets is preferably characterized by collection instructions given bya processing module (e.g., a control printed circuit board) of anelectronics subsystem of the biomonitoring neuroheadset. Additionally oralternatively, Block S120 can include collecting one or morebioelectrical signal datasets according to predetermined (e.g., by amanufacturer, by a user, by a care provider, etc.) and/or automaticallydetermined (e.g., based on a computational model, thresholds, etc.)collection instructions and/or parameters. However, collecting one ormore bioelectrical signal datasets can be characterized by any suitablecriteria.

With respect to temporal aspects relating to Block S120, collecting oneor more bioelectrical signal datasets can be performed during,associated with, and/or correspond to any suitable temporal indicator(e.g., time point, time window, time period, duration, etc.). Timeperiods can be of any suitable length (e.g., on the order of seconds,minutes, hours, days, etc.). In variations, Block S120 can be performedin response to (e.g., instantaneously, substantially instantaneously,etc.) events such as an audible signal (e.g., a detected audio signal,provided audio content, etc.), a visual signal (e.g., a detected visualsignal, displayed visual content such as an image, etc.), and any othersuitable event. In a specific example, Block S120 can includecollecting, at an EEG sensor, an EEG signal dataset from the user duringa first time period. Additionally or alternatively, collecting one ormore bioelectrical signal datasets S120 can be performed during timeperiods in which the user is performing a specific activity. Specificactivities can include any combination of: engaging in content (e.g.,digital content, television, music, film, video games, etc.),interacting with other individuals (e.g., during conversation, at asocial activity, at a workplace activity), daily activities (e.g., athome, at work, during sleep, during meals, etc.), and/or any othersuitable activity. However, collecting one or more bioelectrical signaldatasets can be performed at any suitable time as the user performs anysuitable action.

In relation to temporal aspects of Block S120 and other portions of themethod 100, time points (e.g., time stamps, temporal markers indicativeof regions of the bioelectrical signal datasets, etc.) can include: astart or stop time of a piece of media content (e.g., a video observedby the user), detection of the user making a gesture, and other periodsof time of low specificity. Additionally or alternatively, time pointscan be of high specificity (e.g., indicative of the exact moment ofonset of a visual, audible, or other stimulus). High specificity timepoints can be utilized to detect responses in the bioelectrical signals(e.g., spontaneous involuntary brain responses, evoked potentials,etc.). Such responses can be extracted in the course of a single trial(e.g., single bioelectrical signal collection), repeated trials (e.g.,repeated application of identical stimuli and subsequent signalaveraging to improve signal-to-noise ratio of the evoked potentialresponse above background bioelectrical signals), or otherwise suitablyextracted. In cases wherein repeated trials are utilized, highspecificity time localization of response to stimuli can be used todetermine and/or label bioelectrical signal portions corresponding tothe stimulus response (e.g., label time locked stimuli corresponding toone sample period, such as 3 milliseconds for a 256 Hz sampling rate,etc.).

Regarding Block S120, additionally or alternatively, collecting one ormore bioelectrical signal datasets can include any elements disclosed inU.S. patent application Ser. No. 13/903,861 filed 28 May 2013, U.S.patent application Ser. No. 14/447,326 filed 30 Jul. 2014, and U.S.patent application Ser. No. 15/058,622 filed 2 Mar. 2016, which arehereby incorporated in their entirety by this reference. However, BlockS120 can be performed in any suitable manner.

Regarding Block S120, the collected bioelectrical signal datasets arepreferably differential measurements referenced to a reference value,but can alternatively be the signal (e.g., voltage) sampled at abiosensor (e.g., bioelectrical sensor) or be any other suitable set ofsignals. The reference value is preferably determined by collecting oneor more reference signal datasets at one or more reference sensors, asdescribed further below.

3.3 Collecting a Reference Signal Dataset.

As shown in FIGS. 1-2 , Block S124 recites: collecting one or morereference signal datasets, which functions to collect reference signaldata against which to reference the bioelectrical signal datasetscollected in Block S120 (e.g. in a differential potential measurement,individually referencing, referencing after averaging the referencesignal datasets as in Block S130, etc.). Collecting one or morereference signal datasets is preferably performed with one or morereference electrodes (e.g., arranged proximal the sensor, the biosensorelectrode, the active electrode, EEG electrode, etc.), common modesensors, and/or one or more DRL electrodes or modules. In a specificexample, Block S124 can include collecting, at a common mode sensor, acommon mode signal dataset, where the common mode signal dataset canenable detection and removal of common-mode components of noise tofacilitate downstream signal processing. Additionally or alternatively,one more collected reference signal datasets can include any suitableamount, type and/or combination of reference signals for reducing noiseassociated with any suitable dataset related to the method 100.

With respect to temporal aspects relating to Block S124, collecting oneor more reference signal datasets is preferably performed in parallel,simultaneously, and/or contemporaneously with collecting one or morebioelectrical signal datasets as in Block S120. As such, Block S124 ispreferably performed during the same temporal indicator (e.g., duringthe same time period) at which Block S120 is performed. In a specificexample, Block S124 can include collecting, at a common mode sensor, acommon mode signal dataset contemporaneously with collecting an EEGsignal dataset during a time period. In a second specific example, BlockS124 can include sampling (and/or collecting), a first and secondreference signal dataset with a first and second reference sensor,respectively, during the same time period as EEG signal dataset sampling(and/or collecting) with the sensor (e.g., EEG sensor). In this specificexample, at least one of the first and second reference sensors iscollocated (e.g., within a predetermined distance) with the sensor(e.g., EEG sensor). Additionally or alternatively, reference signaldatasets can be collected concurrently, contemporaneously, in serial,before, after, and/or with any other suitable relationship to otherportions of the method 100. The reference sensors are preferably commonmode sensors (e.g., CMS biopotential electrodes), but can alternativelybe ground sensors (ground electrodes) or be any suitable sensor (e.g.,any subset of the channels of a 10:20 EEG system).

In a variation of Block S124, collecting reference signal datasets caninclude collecting a plurality of reference signal datasets at a set ofreference sensors of the biomonitoring neuroheadset. For example, thebiomonitoring neuroheadset can include a set of two reference sensors(e.g., a first reference sensor or electrode proximal a left ear region,and a second reference sensor or electrode proximal a right ear region).In this example, Block S124 can include collecting a first referencesignal dataset at the first reference sensor, and collecting a secondreference signal dataset at the second reference sensor. The first andsecond reference signal datasets can be common mode signal datasets, orbe other reference signal datasets. The first and second referencesensors can be common mode sensors, or be other reference sensors. Inanother example, the biomonitoring neuroheadset can additionally oralternatively include a DRL module including a set of DRL electrodes(e.g., a first DRL electrode proximal a first common mode sensor and aleft ear region, and a second DRL electrode proximal a second commonmode sensor and a right ear region). In examples with a plurality ofreference sensors, reference signal datasets can be collected with theplurality of reference sensors contemporaneously, simultaneously,serially, and/or with any suitable temporal relationship.

In a second variation, S124 can include collecting the reference signaldatasets from a single reference sensor. In this variation, the system(e.g., biomonitoring headset) can include a single reference sensor, orinclude multiple reference sensors. When the system includes a singlereference sensor, the reference sensor can be located proximal one ormore of the biosensors (active sensors) (e.g., the reference sensor canbe located on the ear flap adjacent to the canal sensor), or be locateddistal all of the active sensors. When the single reference sensor isarranged proximal a biosensor (active sensor), the proximal bioelectrodesensor may measure an attenuated signal (e.g., both the reference sensorand the ear canal can share brain signals as well as body potential to asignificant extent), while a distal bioelectrode sensor may measure anamplified (or more accurate) signal. In this embodiment, the method canselectively use: only the distal biosensor's measurement (e.g.,optionally including an independent floating measurement circuit foreach distal biosensor), only the proximal biosensor's measurement,determine the bioparameter from a weighted calculation of the proximaland distal bioelectrode sensors' measurements, determine an average ofthe reference signals and comparing each biosensor's measurement againstthe average, or otherwise use the bioelectrode sensors' measurements.

Block S124 can additionally or alternatively include any elementsdisclosed in U.S. patent application Ser. No. 14/447,326 filed 30 Jul.2014, which is hereby incorporated in its entirety by this reference.However, Block S124 can be performed in any suitable manner.

3.4 Generating a Reference Signal Dataset.

As shown in FIG. 2 , Block S130 includes: generating a reference signaldataset from the one or more reference signal datasets (e.g., collectedin Block S120, Block S124, etc.). The reference signal dataset ispreferably a combined reference signal dataset (e.g., an averagedreference signal dataset), wherein the reference signals from multiplereference sensors and/or a time series of reference signals fromindividual reference sensor(s) are combined into the combined referencesignal dataset, but can additionally or alternatively include anindividual reference signal dataset (e.g., including a single referencesignal value for a single timepoint; a reference signal for a singlereference sensor; etc.). Block S130 functions to generate a referencepotential (e.g., common mode potential) for bioelectrical potentialmeasurements (e.g., EEG measurements, ECG measurements, bioelectricalsignal datasets, etc.). Block S130 can also function to produce areference signal that averages reference potential characteristics frommultiple body locations. Block S130 is preferably performed by anaveraging circuit as described below in Section 4, but can additionallyor alternatively be otherwise suitably performed by any suitablecomponent or process (e.g., in the digital domain).

In variants, S230 generates a more accurate common mode body potentialsignal, such as by combining the reference signals measured fromdifferent parts of the user (e.g., from different sides of the user).For example, the left and right reference signals can be averaged, suchthat the left and right EEG components in the averaged signal can besignificantly reduced by this process, while the true common componentsthat appear in both sides are retained. The reference signals (e.g., areference signal dataset) are preferably combined over time (e.g.,reference signals from a time series are combined), but can additionallyor alternatively be combined for a single time point (e.g., samplingtimepoint or sampling duration). The reference signals are preferablysampled by a plurality of reference sensors, but can additionally oralternatively be sampled by a single reference sensor. The referencesignals can be combined by averaging (e.g., unweighted average, weightedaverage, etc.), or otherwise combined. The reference signals can beinstantaneously combined (e.g., simultaneously averaged) in real- ornear-real time (e.g., during system operation), but can additionally oralternatively be averaged asynchronously (e.g., wherein the channelsignals can be referenced against historical signals or a laggingaverage). In a specific example, the reference signals can be combinedin real time, such that the EEG signals measured at each time instantcan be the difference between the measured EEG channel potential) atsaid time instant) and the averaged reference potential at said timeinstant. However, the reference signal dataset can be otherwisedetermined.

In variations of Block S130, a plurality of reference signal datasetscollected by a set of reference signal sensors can be used in producingan averaged reference signal dataset. Block 8132 can include processinga plurality of reference signal datasets into a single combinedreference signal dataset. Block 132 (and/or Block 130) is preferablyperformed in real- or near-real time (e.g., as the reference signaldatasets are being sampled or collected 8124), but can alternatively beperformed asynchronously with Block S124 (e.g., after S124, after asampling session, etc.), or at any suitable time.

The reference signals that are combined into a single value within thecombined reference signal dataset are preferably concurrently sampled(e.g., share a common or the same timestamp) by different referencesensors (and/or be reference signals from different reference channels),but can alternatively be signals sampled by the same reference sensor(e.g., signals from the same reference channel) sampled across a timeperiod, or be any other suitable set of reference signals.

Processing operations preferably include averaging operations (e.g.,averaging values from multiple reference signal datasets collectedduring a same time period. calculating the combined reference signalvalues using a weighted equation, etc.), but can include any of theprocessing operations described with respect to Block S130, and/or anyother suitable processing operation. The weights in the weightedequation can be determined through calibration, predetermined,determined based on reference sensor proximity to the bioelectricalsensor, sensor parameters (e.g., age, wear), or otherwise determined.

In a first example, generating a combined (e.g., averaged) referencesignal dataset includes performing an averaging operation with a firstreference signal dataset (e.g., collected at a left ear region of theuser during a time period with a left reference sensor) and a secondreference signal dataset (e.g., collected at a right ear region of theuser during the time period with a right reference sensor), and whereproducing a common noise-reduced EEG dataset comprises producing thecommon noise-reduced EEG dataset based on the combined common modesignal dataset. The first and second reference signal datasets arepreferably common mode signals, wherein the first and second referencesensors are preferably common mode sense electrodes; however, the firstand second reference signal datasets can be any suitable dataset, andthe first and second reference sensors can be any suitable referencesensor. This is preferably performed in the analog domain, but canadditionally or alternatively be performed in the digital domain (e.g.,in post processing).

However, utilizing a set of reference signal datasets collected by a setof reference signal sensors can be performed in any suitable manner forgenerating a noise-reduced bioelectrical signal dataset.

Block S130 can include Block S134, which includes: generating a drivenright leg signal. Block S134 functions to reduce common modeinterference (e.g., from electromagnetic interference) in generating acommon mode noise-reduced dataset for downstream analysis. Generating adriven right leg signal preferably includes actively canceling commonmode interference with a driven right leg module of a noise reductionsubsystem of the biomonitoring neuroheadset. The driven right leg moduleis preferably characterized by a feedback reference location at a thirdsubregion of the ear region (e.g., where a bioelectrical signal sensoris positioned proximal a first subregion, and where a reference signalsensor is positioned proximal a second subregion), where producing thenoise-reduced bioelectrical signal dataset includes producing thenoise-reduced bioelectrical signal dataset from the driven right legsignal. The third subregion is preferably at an ear region of the user,but can alternatively be at any suitable anatomical position of theuser. In a specific example, the third subregion includes the mastoidprocess of the temporal bone corresponding to the ear region, but caninclude any other suitable region of the ear. Additionally oralternatively, a driven right leg module can reduce common mode noise inany suitable manner.

In relation to temporal aspects of Block S134, generating a driven rightleg signal is preferably performed contemporaneously with collecting acommon mode signal dataset (e.g., as in Block S124), such that commonmode interference can be canceled by the driven right leg module duringthe time period in which the bioelectrical signal dataset andcorresponding mode signal dataset are collected. However, generating adriven right leg signal can be performed at any suitable time and/orwith any suitable temporal resolution; in particular, temporal aspectsof Block S134 can be performed in an analogous manner to bioelectricalsignal collection (e.g., in accordance with one or more variations ofBlock S120 as described above).

In relation to Block S134, the generated driven right leg (DRL) signalpreferably produces a fast antiphase compensation signal, which canfunction to cancel fast and slow oscillations and drift in the bodypotential. For example, the body potential can oscillate at 50 Hz or 60Hz due to electromagnetic pick-up from electrical mains (e.g., powerlinenoise), and the amplitude of this oscillation may be several volts. Inthis example and related examples, the signal generated by a DRL circuit(e.g., the DRL signal) imposes an exact copy of the body potentialvariation onto the DRL electrode (e.g., a reference sensor), preferablycausing the electronic detection circuit (e.g., that generates thebioelectrical signal dataset) to follow the body oscillations (e.g., asa moving reference value). In this example and related examples, thereference input voltage (e.g., reference signal data) for each EEGsensor can be derived from the mid-rail voltage of the sensor, which hasthe body potential variation imposed on it by operation of the DRLcircuit, and the common mode signal can thereby be removed from thedifference measurement (e.g., the bioelectrical measurement) andvariations due to brain or muscle activity adjacent to the sensor arepassed into the detection circuit for further analysis (e.g., in otherBlocks of the method). Additionally or alternatively, the DRL signal canfunction to drive a reference level (e.g., a reference for adifferential bioelectrical signal measurement) to follow the backgroundbody potential (e.g., to cancel common mode noise). The DRL signal canalso encode a measurable perturbation, which can be detected in eachbioelectrical signal dataset, wherein the amplitude of the detectedperturbation can be proportional to the series conductance of the CMSchain and the sensor (e.g., the amplitude can vary on a sensor by sensorbasis in inverse proportion to the impedance of the electrical contact).In variants, the real-time impedance measurement can be used to providefeedback information to the user, enabling the user to optimize thepositioning and contact quality of individual biopotential electrodes(e.g., based on an impedance measurement-user feedback loop), so as to:improve the quality of the biopotential signals measured, and to reducenoise which may be induced by poor contact quality (e.g., characterizedby a higher than usual contact impedance). The real time impedancemeasurement can be measured and used as described in U.S. applicationSer. No. 16/227,004 filed 20 Dec. 2018 and/or U.S. application Ser. No.______ titled “Method and System for Collecting and ProcessingBioelectrical Signals” and filed 20 Apr. 2019, each of which areincorporated herein in its entirety by this reference, and/or beotherwise measured and used. However, generating and using a DRL signalcan be otherwise suitably performed.

In a variation of Block S134, generating one or more driven right legsignals can be performed by one or more driven right leg modulesincluding a plurality of feedback reference locations. Feedbackreference locations beyond the first are preferably redundant, such thatsufficient contact between the driven right leg module and the user isonly required for a single feedback reference location in order tofacilitate common mode interference reduction. Additionally oralternatively, driven right leg module functionality can be allocatedacross feedback reference locations, such that additional noisereduction can be conferred through sufficient contact between a user andmultiple feedback reference locations. In a specific example, the drivenright leg module can be characterized by a first feedback referencelocation at a third subregion of an ear region (e.g., where a firstbioelectrical signal sensor is positioned at a first subregion, and afirst common mode sensor is positioned at a second subregion), by asecond feedback reference location at a third contralateral subregion(e.g., where a second bioelectrical signal sensor is positioned at afirst contralateral subregion, and a second common mode sensor ispositioned at a second contralateral subregion) of the contralateral earregion, where the third contralateral subregion is proximal the firstand the second contralateral subregions of the contralateral ear region,and where generating the driven right leg signal is in response toadequate contact between the user and the driven right leg module atleast at one of the first feedback reference location and the secondfeedback reference location. However, this variation of Block S134 canbe performed in any suitable manner.

Additionally or alternatively, Block S130 and Block S134 can include anyelements described in U.S. patent application Ser. No. 14/447,326 filed30 Jul. 2014, which is herein incorporated in its entirety by thisreference. However, Block S134 can be performed in any suitable manner.Block S134 is preferably performed by a driven right leg module, asdescribed in Section 4 below, but can additionally or alternatively beperformed using any suitable system and/or components.

3.5 Determining a Bioparameter.

As shown in FIGS. 1-2 , Block S140 recites: determining a bioparameter.Block S140 functions to analyze bioelectrical signals in determining aparameter describing physiological and/or psychological status of auser. Types of bioparameters can include any one or more of: cognitivestate metrics (e.g., attention, cognitive load, stress, fatigue,valence, arousal, engagement, preferences, intentions, etc.),psychological traits or conditions (e.g., impulsivity, resilience,focus, anxiety, depression, chronic stress, etc.), mental state,cardiovascular parameters, diagnostic analyses (e.g., identification ofsymptoms correlated with a diagnosis, etc.), treatment responseparameters (e.g., response to medication, response to therapy, etc.),communication disorders (e.g., expressive language disorder, languageimpairment, autism spectrum disorder, etc.), and/or any other suitablebioparameter descriptive of a physiological and/or psychological statusof a user.

In variations, Block S140 can include: generating an aggregatedbioelectrical signal dataset based on a combination of two or morebioelectrical signal datasets (e.g., collected in Block S120), anddetermining the bioparameter based on the aggregated bioelectricalsignal dataset. However, determining the bioparameter can additionallyor alternatively include determining the bioparameter based on one ormore of the two or more bioelectrical signal datasets in isolation(e.g., non-aggregated).

In relation to Block S140, determining a bioparameter is preferablybased on, determined by, and/or derived from the bioelectrical signaldataset (e.g., based on averaged and/or noise-reduced bioelectricalsignals collected by multiple sensors of a biomonitoring neuroheadset).For example, determining a bioparameter can include extracting signalfeatures and/or patterns from a noise-reduced bioelectrical signaldataset(s); extracting bioelectrical features (e.g., EEG-relatedfeatures) from bioelectrical signals (e.g., EEG signals) associated withthe noise-reduced bioelectrical signal dataset(s); and processing thebioelectrical features with a bioparameter model to determine one ormore bioparameters for the user; or otherwise determining abioparameter. The noise-reduced bioelectrical signal dataset can be: acommon noise-reduced EEG dataset (e.g., a left dataset, a right dataset,a composite dataset generated from the left and right datasets, etc.),the raw bioelectrical signal dataset collected in S120, or be any othersuitable bioelectrical dataset.

Additionally or alternatively, data associated with any suitable datasetcan be used in generating a bioparameter. In variations, determining oneor more bioparameters can include generating and/or implementing one ormore bioparameter models. Generating a bioparameter model can includeprocessing any one or a combination of probabilities properties,heuristic properties, deterministic properties, rule-based properties,and/or any other suitable feature algorithmically to determinebioparameters of a user. In a specific example, Block S140 can includetraining a machine learning bioparameter model based on audio features,bioelectrical signal features (e.g., EEG, heart rate, and heart ratevariability), kinematic features (e.g., motion sensor outputs indicativeof head gestures, balance, gait, steps, activities, 3D movement, etc.),and corresponding bioparameter labels; predicting a bioparameter for auser from using the trained machine learning bioparameter model withuser audio feature inputs and user bioelectrical signal feature inputs.Audio features can include features from output audio (e.g.,user-initiated sounds, such as speech, singing, tones, clicks,responses, instructions, verbal commands, etc.; music; etc.), inputaudio (e.g., stimuli, such as incoming sounds, tones, music, others'speech, etc.), or from any suitable audio source. Additionally oralternatively, machine learning approaches can include any suitablemachine learning approach. However, determining a bioparameter can beperformed with any suitable approach.

Regarding Block S140, determining a bioparameter is preferably performedby a remote server, but portions of Block S140 can additionally oralternatively be performed by a biomonitoring neuroheadset (e.g.,onboard the neuroheadset), another user device (e.g., a user smartphone,a user computer, etc.), and/or any other suitable component. Determininga bioparameter S140 is preferably performed after generating anoise-reduced bioelectrical signal dataset, but can alternatively oradditionally be performed at any suitable time. For example, Block S140can be performed in response to receiving, at a remote server, dataassociated with the noise-reduced bioelectrical signal dataset.Additionally or alternatively, determining a bioparameter can beperformed before generating a noise-reduced bioelectrical signaldataset, or at any suitable time. Bioparameters of different types canbe generated contemporaneously, in parallel, in serial, and/or with anysuitable time relationship. For example, the method 100 can includegenerating a cognitive state metric and a cardiovascular parameter for auser contemporaneously during a time period. However, portions of BlockS140 can be performed by an suitable component at any suitable time.

In specific examples, Block S140 can additionally or alternativelyinclude testing for perceptual and/or cognitive function. Examples ofpsychological and/or physiological functions that can be tested caninclude one or more of: deafness, low level brain function, mentaldisorders, sensory disorders, and/or any other suitable perceptualand/or cognitive function. Testing for perceptual and/or cognitivefunction can include providing a stimulus, such as: emitting an audiosample (e.g., as in variations of Block S180) with one or more speakersof the biomonitoring neuroheadset; collecting bioelectrical signal data(e.g., with an EEG sensor) contemporaneously with emitting the audiosample; and generating a perceptual and/or cognitive function parameterdescribing the functionality of a psychological and/or physiologicalaspect of the user. Testing for perceptual and/or cognitive function canadditionally or alternatively include providing various other stimuli,including tactile stimuli, visual stimuli, and any other suitablesensory stimuli, and collecting bioelectrical signal data and/orauxiliary data (e.g., reaction or response features, such as intensity,reaction time, response time, etc.) in conjunction with providing thestimulus and/or stimuli to thereby generate a perceptual and/orcognitive function parameter in any other suitable manner. Perceptualand/or cognitive function parameters can be generated based on multipleEEG datasets (e.g., where the data is averaged over the repeatedacquisitions) collected contemporaneously with emission of a set ofaudio samples (e.g., a set of different audio samples, a set of the sameaudio sample repeated multiple times, etc.), but can additionally oralternatively be generated based on, determined by, and/or derived fromany suitable data (e.g., supplemental sensor data, etc.). However,testing for perceptual and/or cognitive function can be performed in anysuitable manner.

In a variation of Block S140, determining one or more bioparameters canadditionally or alternatively include determining one or more cognitivestate metrics S142. A cognitive state metric preferably indicates acognitive state of a user. Cognitive state can include one more of:mood, emotional state, psychological health, focus level, thoughtprocesses, language abilities, memories, reasoning abilities, and/or anysuitable cognitive state. Determining a cognitive state is preferablybased on, determined by, and/or derived from audio data andbioelectrical data associated with at least one of: raw datasets (e.g.,collected bioelectrical signal datasets as in Block S120, referencesignal datasets as in Block S124), processed datasets (e.g.,noise-reduced bioelectrical signal datasets, aggregated bioelectricaldatasets, driven right leg signals, etc.), cardiovascular parameter data(e.g., heart rate and/or heart rate variability), user kinetic behaviordata (e.g., indicative of user motion, gait, balance, gestures, etc.),and/or any other suitable data. In a specific example, Block S142 caninclude receiving, at a remote server, an EEG dataset (e.g.,common-noise reduced EEG dataset) and cardiovascular parameter data; andgenerating a cognitive state metric based on the EEG dataset and thecardiovascular parameter, wherein the cognitive state metric indicates acognitive state of the user during a time period (e.g., a time period inwhich the raw EEG dataset and cardiovascular parameter were collected).Additionally or alternatively, determining a cognitive state metric caninclude any elements described in U.S. application Ser. No. 13/903,832,filed 28 May 2013, and U.S. application Ser. No. 15/058,622 filed 2 Mar.2016, each of which are herein incorporated in their entirety by thisreference. However, determining a cognitive state metric can beperformed in any suitable manner.

In another variation of Block S140, determining one or morebioparameters can additionally or alternatively include determining oneor more cardiovascular parameters. Cardiovascular parameters can includeone or more of: heart rate, heart rate variability, blood pressure,blood pressure variability, blood flow, heartbeat signatures, measuresof blood vessel stiffness, measures indicative of atherosclerosis orother cardiovascular disease, other measures of cardiovascular risk,and/or any other suitable cardiovascular parameter. A specific exampleis shown in FIG. 16 (e.g., showing extracting heart features, such asECG artifacts, from the EEG signal). Determining one or morecardiovascular parameters is preferably based on, determined by, and/orderived from bioelectrical signal features associated with abioelectrical signal dataset (e.g., collected as in Block S120, anoise-reduced signal dataset (e.g., generated as in Block S144),bioelectrical signal portions of a combined noise and bioelectricalsignal processed dataset (e.g., generated as in Block S130), and/or anysuitable datasets including bioelectrical signal features. Additionallyor alternatively, determining one or more cardiovascular parameters canbe based on audio signal features (e.g., breathing patterns) extractedfrom datasets associated with any suitable dataset. However, anysuitable cardiovascular parameters can be generated from any suitabledata.

In variations, determining one or more cardiovascular parameters ispreferably based on detecting a slow oscillation in collected EEGsignals, the slow oscillation arising from blood flow in sync with apulse of a user. Collected EEG signals (e.g., noise-reduced EEG signalsof a combined audio and EEG processed dataset) also preferably includePQRST complex sequences (e.g., analogous to PQRST spike sequencesobserved in ECG signals). In a specific example, Block S164 can includeidentifying a blood flow time-varying oscillation in noise-reducedvalues of a noise-reduced EEG dataset (e.g., a common noise-reduced EEGdataset); and estimating at least one of a heart rate and a heart ratevariability based on the blood flow time-varying oscillation innoise-reduced values, where the at least one of the heart rate and theheart rate variability corresponds to a time period (e.g., a time periodin which the raw EEG dataset was collected). In this specific example,identifying the blood flow time-varying oscillation in noise-reducedvalues can include identifying a set of QRS complex sequences in thenoise-reduced values of the noise-reduced EEG dataset, and whereestimating the at least one of the heart rate and the heart ratevariability is based on the set of QRS complex sequences. In anotherspecific example, Block S164 can include searching (e.g., patternmatching) for specific characteristics in the time-varying oscillation(e.g., overlaid on the background EEG signal), which can includepatterns such as a PQRS complex, a slow reproducible oscillation inbackground voltage in the range 0.75-3 Hz, and any other suitablepattern that can be associated with cardiovascular parameters (e.g.,heart beat, pulse, etc.) However, determining one or more cardiovascularparameters can be performed in any suitable manner.

Block S140 can include Block S144, which includes: generating anoise-reduced bioelectrical signal dataset. S144 is preferably performedbefore S140, but can alternatively be performed after. Producing one ormore noise-reduced bioelectrical signal datasets preferably includesproducing a noise-reduced EEG signal dataset, but can alternativelyinclude producing noise-reduced bioelectrical signal datasets of anyother suitable type. The noise-reduced bioelectrical dataset ispreferably a common noise-reduced bioelectrical signal dataset (e.g.,with common noise removed) but can alternatively be a dataset that isotherwise processed or noise-reduced.

Block S144 functions to process a bioelectrical signal dataset (e.g., anEEG signal dataset collected as in Block S120; an aggregatedbioelectrical signal dataset, etc.) with a reference signal dataset(e.g., the averaged reference signal dataset as generated in Block S130,a common mode signal dataset collected as in Block S124, a DRL signaldataset, etc.) in order to remove noise from one or more bioelectricalsignal datasets beyond that which was removed by referencing thecollected bioelectrical signals to the averaged reference signaldataset.

In one example, the noise-reduced bioelectrical signal dataset can bedetermined by subtracting the combined reference signal dataset from theleft and/or right bioelectrical signal datasets, or be otherwisedetermined. In a specific example, a left noise-reduced dataset can begenerated by subtracting the combined reference signal values (of thecombined reference signal dataset) from left bioelectrical signal values(of the left bioelectrical dataset), wherein each combined referencesignal value is subtracted from a left bioelectrical signal valuesharing a timestamp (or has a substantially similar timestamp, within amargin of error) with the respective combined reference signal value(example shown in FIG. 15 ). This can be repeated with the rightbioelectrical dataset and the combined reference signal dataset togenerate a right noise-reduced dataset. However, the noise-reducedbioelectrical signal dataset can be otherwise determined.

The method can optionally include post-processing the noise-reducedbioelectrical signal dataset, which functions to further de-noise thebioelectrical signals. This can function to pick up individual noisecharacteristics, or which may enable derivation of channel specificnoise. This is preferably performed after referencing the differentialmeasurement (e.g., the bioelectrical signal dataset) to the averagedreference signal, based on individual reference signals. In suchvariations, Block S144 can result in determining individual noisecharacteristics (and, in examples, eliminating the determined noisecharacteristics from the bioelectrical signal dataset), and/or derivingchannel specific noise characteristics (and, in examples, modeling thechannel noise, eliminating the channel noise, compensating for thechannel noise, etc.). However, a noise-reduced version of any suitablebioelectrical signal dataset and/or other dataset can be generated.

Post-processing one or more noise-reduced bioelectrical signal datasetspreferably includes using one or more reference signals to filter,subtract, and/or otherwise eliminate noise present in one or morebioelectrical signal datasets. For example, producing a noise-reducedbioelectrical signal dataset can include reducing common mode noise(e.g., noise conducted on lines in the same direction). Additionally oralternatively, post-processing the noise-reduced bioelectrical signaldatasets can include reducing differential mode noise (e.g., conductedon lines characterized by opposite directions), random noise, coherentnoise, and/or any other suitable type of noise. Bioelectrical signaldatasets can include various signal artifacts caused by user musclemotion; for example, muscle contraction and/or expansion can generatebioelectrical signals, which can occur near a bioelectrical sensor andinduce a signal (e.g., a high amplitude signal) corresponding to themuscle contraction and/or expansion. In other examples, motion can causemovement of sensors (e.g., electrode contact points) across the skinand/or movement of the subcutaneous tissue beneath the sensor, which canalso induce signal artifacts. Such signal artifacts can be removed viacombination with reference signal datasets (e.g., in the analog domain,in the digital domain, etc.), source localization methods (e.g.,independent components analysis), and/or other suitable signalprocessing techniques. In one example, post processing can be enhancedby considering contextual parameters, such motion sensor outputs oraudio features (e.g., recorded by the microphone), which can applycontext to the noise reduction algorithms (e.g. by adjusting thresholdsfor motion artifact cancellation in the EEG signal based on motionsensor outputs). However, reference signal datasets can be used in anysuitable manner for reducing noise.

3.6 Controlling Operation of a User Device.

As shown in FIGS. 1 and 3 , the method 100 can additionally oralternatively include Block S110, which recites: controlling operationof a user device. Block S110 functions to instruct a user device toperform one or more operations based on analysis of at least one ofbioelectrical signal data (e.g., EEG signal data) and bioparametersderived therefrom collected in association with the biomonitoringneuroheadset. Controllable user devices preferably include thebiomonitoring neuroheadset, a user device (e.g., a mobile computingdevice, a computer, etc.) in communication (e.g., wired communication,wireless communication, etc.) with the biomonitoring neuroheadset, andother suitable mobile devices, but can additionally or alternativelyinclude a smart appliance (e.g., an internet-enabled television, videogame console, cooking appliance, exercise device, etc.), and/or anyother suitable device. In a specific example including control of thebiomonitoring headset, the method 100 can include transmitting acombined audio and EEG processed dataset to a computing device of theuser; generating, at a software component executing on the computingdevice, an analysis of an audio signal portion of the combined audio andEEG processed dataset; receiving, at the biomonitoring neuroheadset,operation instructions transmitted by the computing device and generatedbased on the analysis of the audio signal portion; and operating thebiomonitoring neuroheadset based on the operation instructions. In aspecific example including control of an external device using thebiomonitoring headset, the method 100 can include transmitting an EEGprocessed dataset to a computing device of the user; generating, at asoftware component executing on the computing device, an analysis of theEEG processed dataset to extract a mental command; receiving, at anexternal device, operation instructions transmitted by the computingdevice and generated based on the mental command; and operating theexternal device based on the operation instructions (e.g., changing amusical track being played by the external device, turning on the powerof the external device, turning off the power of the external device,etc.).

The EEG processed dataset can be associated with a cognitive state, amental command, and/or any other suitable bioparameter (e.g., whereinthe association between the EEG processed dataset and the bioparametercan be determined as disclosed herein, or otherwise determined). Thebioparameter(s) can each be associated with one or more operationinstructions, endpoints, and/or other output, which can enable thesystem to operate as a passive and/or active BCI (brain-computerinterface). In an example of active BCI operation, the EEG processeddataset can be determined to be associated with a mental command, suchas skipping to the next music track. In this first example, S110 caninclude controlling an endpoint (e.g., music player) to skip to the nextmusic track. In an example of passive BCI operation, the EEG processeddataset can be determined to be associated with a mental or emotionalstate (e.g., fatigue), wherein S110 can include controlling a set ofendpoints (e.g., coffee maker, computer, etc.) to perform operationsassociated with the mental or emotional state (e.g., controlling themusic player to change the music to a high-BPM or high-energy genre,starting the coffee maker, sending a notification to the user's deviceto instruct the user to take a break or exercise; wherein the operationscan be pre-associated with the mental or emotional state). However, S110can be otherwise performed.

Regarding Block S110, controllable operations for a user device caninclude: power operations (e.g., turning on/off, charging, batterymodes, etc.), data collection operations (e.g., controlling abiomonitoring neuroheadset to collect bioelectrical signal datasets,reference signal datasets, audio signal datasets, etc.), controllingapplications executable on the user device (e.g., controllingapplications related to alarm, navigation, weather, timer,user-downloaded applications, calls, voicemail, location, email,schedule, entertainment, health & fitness, news, social, music,messaging, communication, etc.), operating transceivers of the device(e.g., controlling a user device to transmit a dataset, configuring auser device to receive a dataset, etc.), and/or any other suitableoperation associated with a user device. In a specific example, BlockS110 can include controlling (e.g., using mental commands, using amicrophone of the biomonitoring neuroheadset, using a combination ofcollected EEG data and auxiliary data, using pure EEG processeddatasets, etc.), operations associated with a mobile computing device(e.g., smart phone) of a user, including at least one of: phone callingfeatures, web meeting features, voice recording (e.g., voice memo)features, virtual assistant features, voice-to-text features, and/or anyother suitable features associated with a mobile computing device.However, any suitable operation can be controlled with respect to anysuitable user device.

In relation to Block S110, controlling operation of a user device ispreferably based on, determined by, and/or derived from analysis ofaudio and/or bioelectrical signal data associated with the combinedaudio and bioelectrical signal processed dataset, but can additionallyor alternatively be associated with any suitable dataset. Regardinganalysis of audio data for controlling operation of a user device,analyses can include: speech recognition approaches (e.g., using HiddenMarkov models, machine learning models such as those described inSection 4, neural networks, dynamic time warping, etc.), audio signalprocessing, and/or any other suitable analyses. Regarding analysis ofbioelectrical signal data for controlling operation of a user device,analyses can include: bioelectrical signal processing (e.g., approachesdescribed with respect to Block S130, S140, etc.), cognitive statemetric analyses (e.g., as in Block S142), and/or any other suitableanalyses for determining user intent based on bioelectrical signals.Additionally or alternatively, controlling operation of a user devicecan be based on any suitable data and/or approach. In a specificexample, Block S110 can include: extracting audio features from audiosignal data associated with a combined audio and bioelectrical signaldataset; extracting bioelectrical signal features from bioelectricalsignal data associated with the combined audio and bioelectrical signaldataset; generating user device control instructions based on theextracted audio features and bioelectrical signal features; transmittingthe control instructions to the user device (e.g., the user device forwhich the control instructions were generated), the control instructionsconfigured to instruct operation of the user device.

However, controlling operation of a user device can be performed in anysuitable manner.

3.8 Determining a Stimulus.

As shown in FIGS. 1 and 3 , the method 100 can additionally oralternatively include Block S180, which recites: determining a stimulusto modify the cognitive state of the user. Block S180 functions todetermine a stimulative output that can be provided to the user in orderto promote a therapy, facilitate physiological and/or psychologicalmonitoring (e.g., a user response to the stimulus), provoke a desiredreaction, and/or for any other suitable purpose. A stimulus in thiscontext can include various stimuli and/or stimulative therapies, suchas non-medical applications of a therapy, medical applications,self-improvement, training (e.g., physical training, mental training,etc.), wellness applications, and any other suitable applicationsrelated to provided stimuli. In variations, the stimulus can include anaudio therapy, and providing the stimulus can include emitting, at aspeaker of the biomonitoring neuroheadset, an audio sample. In suchvariations, Block S180 can function to output audio at one or morebiomonitoring neuroheadset speakers to promote a therapy and/orfacilitate physiological and/or psychological monitoring (e.g., a userresponse to the outputted audio) of the user. Determining a stimulus caninclude selecting, calculating, estimating, or otherwise determining thestimulus for any one or more of: promoting an audio, visual, haptic,and/or other suitable therapy (e.g., a cognitive behavioral therapyaudio session, etc.), monitoring a user response to the therapy (e.g.,monitoring a user response to an auditory component of media contentthat the user is engaging in, etc.), emitting an output (e.g., an audiosample, an image, a video sample, etc.) in response to user instruction(e.g., a user instructing that a particular song stored on the user'ssmartphone be played, etc.), and/or for any suitable purpose.Additionally or alternatively, S180 can include: providing a stimulusfor any of the purposes disclosed above. In examples wherein determiningthe stimulus includes emitting an audio sample, emitting an audio samplecan additionally or alternatively include selecting an audio sample toemit based on audio data and/or bioelectrical signal data (e.g., EEGsignal data and/or bioparameters) associated with a dataset described inrelation to any Blocks of the method 100, and/or any suitable dataset.However, determining a stimulus can be based on any suitable data.

In a variation of Block S180, determining a stimulus can be based on acognitive state metric generated as in Block S142. For example, BlockS180 can include determining an audio therapy to modify the cognitivestate of the user, based on the cognitive state metric; and promoting,at a speaker of the biomonitoring neuroheadset, the audio therapy to theuser. In a specific example, emitting an audio sample can include, inresponse to generating a cognitive state metric indicating a negativeemotional state of the user, emitting an audio sample characterized byaudio features configured to invoke a positive emotional state of theuser. However, emitting an audio sample based on a cognitive statemetric can be performed in any suitable manner. In another example, thecognitive state metric can be workplace productivity (e.g., focusmetrics above a predetermined global, context-, task-, or user-specificthreshold), stress reduction (e.g., stress metrics below a predeterminedglobal, context-, task-, or user-specific threshold), distraction (e.g.,distraction metrics below a predetermined global, context-, task-, oruser-specific threshold), safety, and/or any other suitable cognitivestate metric, wherein the stimulus can include triggers and/or alerts(e.g., audio, video, haptic stimulus, electric stimulus, etc.) output bythe system and/or connected system (e.g., user device connected to thesystem).

In another variation of Block S180, determining a stimulus canadditionally or alternatively include determining a stimulus based onone or more cardiovascular parameters (e.g., generated as describedabove). For example, determining a stimulus can include selecting anaudio sample based on matching an audio feature (e.g., beats per minute,music genre, types of instruments, vocals, date of publication, audiowaveform features, etc.) with a cardiovascular feature one or morecardiovascular parameters. In a specific example, for a user engaging inphysical activity (e.g., jogging), determining a stimulus can includeselecting a song with a beats per minute feature approximately matchinga heart rate (e.g., an instantaneous heart rate, an average heart rateover a time period, etc.) of the user. In another example, an audiotherapy can be selected and/or promoted based on a one or morecardiovascular features. For example, a soothing audio sample can beemitted at a speaker of a biomonitoring neuroheadset in response togeneration of a cardiovascular parameter indicating a highcardiovascular risk (e.g., high blood pressure, increased heart rate,irregular heart rate, heart rate variability deviating beyond apredetermined global and/or user-specific range, etc.). However,emitting an audio sample based on one or more cardiovascular parameterscan be performed in any suitable fashion.

In another variation of Block S180, determining a stimulus can based ondata characterized by a plurality of data types (e.g., cardiovasculardata, bioparameters generated as in Block S160, cognitive state data,EEG data, etc.) For example, emitting an audio sample can includeselecting an audio sample based on an analysis of one or more cognitivestate metrics (e.g., generated as in Block S142) and one or morecardiovascular parameters (e.g., generated as Block S164). In a specificexample, the method 100 can additionally or alternatively includegenerating a cognitive state metric based on EEG data associated with acombined audio and EEG dataset; generating a cardiovascular parameterbased on the EEG data associated with the combined audio and EEGdataset; identifying one or more target health statuses (e.g., apsychological state, a physiological state) based on the cognitive statemetric and the cardiovascular parameter; and emitting an audio sampleconfigured to facilitate user achievement of the one or more targethealth statuses. In another specific example, the method 100 can includeidentifying a stressed user state based on a cognitive state metric(e.g., a metric indicating emotions of frustration) and a cardiovascularparameter (e.g., a high blood pressure); selecting an audio sampleincluding audio features associated with a relaxed emotional state; andemitting the audio sample at one or more speakers of a biomonitoringneuroheadset. However, emitting an audio sample based on data typifiedby a plurality of data types can be performed in any suitable manner.

Regarding Block S180, in variations, Blocks S120, S124, S130, S140and/or other suitable portions of the method 100 can be performedcontemporaneously with, in parallel with, serially, and/or in responseto emitting an audio sample at a speaker of the biomonitoringneuroheadset S180. In a specific example, the method 100 can include, inresponse to promoting an audio therapy to the user at the speaker:collecting, at a first EEG sensor, a second EEG signal dataset (e.g.,where the first EEG signal dataset was collected prior to emission ofthe audio therapy) from the user during a second time period (e.g.,where the first time period corresponded to collection of the first EEGdataset); collecting, at the first common mode sensor, a second commonmode signal dataset (e.g., where the first common mode signal datasetwas collected during the first time period) contemporaneously withcollecting the second EEG signal dataset during the second time period;collecting, at the microphone, a second audio signal dataset (e.g.,where the first audio signal dataset was collected during the first timeperiod) from the user contemporaneously with collecting the second EEGsignal dataset and the second common mode signal dataset during thesecond time period; and generating a second combined audio and EEGprocessed dataset based on the second EEG signal dataset, the secondcommon mode signal dataset, and the second audio signal; and generatinga second cognitive state metric based on the second combined audio andEEG processed dataset, wherein the second cognitive state metricindicates a cognitive state response to the audio therapy during thesecond time period. However, Block S180 can have any suitablerelationship with other portions of the method.

With respect to temporal aspects relating to Block S180, emitting anaudio sample is preferably performable in real-time or near real-time.For example, emitting an audio sample can be performed during a timeperiod in which bioelectrical signal data and/or audio signal datatriggering the emission of the audio sample (e.g., audio signal dataincluding user speech instructions for the emission of a specified audiosample) were collected. Additionally or alternatively, emitting an audiosample can be performed at any suitable time in relation to portions ofthe method 100, and/or at any suitable time.

However, emitting an audio sample at one or more speakers of thebiomonitoring neuroheadset S180 can be performed in any suitablefashion.

3.9 Additional Blocks

As shown in FIG. 1 , the method 100 can additionally or alternativelyinclude Block S150, which recites: monitoring contact quality of the atleast one or more sensors. Block S150 functions to facilitate highquality sensor signals through adequate coupling between the user andone or more sensors of a biomonitoring neuroheadset. Contact quality ispreferably monitored for one or more bioelectrical signal sensors (e.g.,contact quality between an EEG sensor and an ear canal region) and/orreference signal sensors (e.g., contact quality between a common modesensor and an ear region proximal the temporal bone; contact qualitybetween a DRL electrode and an ear region proximal the temporal bone,etc.). However, monitoring contact quality can be performed for anysuitable sensor; contact quality is preferably monitored for each andevery sensor independently, but can additionally or alternatively bemonitored for all of the sensors collectively, subgroups of sensors, orotherwise suitably monitored. Monitoring contact quality is preferablyperformed for a sensor with a target position proximal an ear region ofa user, but can additionally or alternatively be performed for sensorswith target positions at any suitable anatomical position of a user.However, monitoring contact quality can be performed for any suitablesensor at any suitable location.

Contact quality monitoring is preferably performed substantially asdescribed in U.S. patent application Ser. No. 15/209,582, filed 13 Jul.2016, which is incorporated in its entirety herein by this reference.Additionally or alternatively, Block S150 can include any elementsdescribed in U.S. patent application Ser. No. 12/270,739, filed 13 Nov.2008, which is herein incorporated in its entirety by this reference.However, Block S150 can be performed in any suitable manner.

The method 100 can additionally or alternatively include Block S135,which recites: determining auxiliary data. Auxiliary data is preferablydetermined at the biomonitoring headset, but can additionally oralternatively be determined at any suitable location. Auxiliary datapreferably includes audio data, and is preferably collected by amicrophone of the biomonitoring headset, but can additionally oralternatively include any suitable type of data local to thebiomonitoring headset (e.g., video data collected by a video camera,temperature data collected by a thermometer, motion data collected by anaccelerometer, GPS data collected by a connected user device, socialmedia activity, calendar activity, user event occurrence, etc.).Examples of user events can include: attendance at a concert, watchingor participating in a sporting event, watching broadcast mediacontemporaneously with other users, or any other suitable event. Theauxiliary data gathered in Block S135 can, in variations, form the basisin whole or part for any of the analyses previously described (e.g.,determining a cognitive state metric for the user).

The method 100 can, however, include any other suitable blocks or stepsconfigured to collect, monitor, and/or analyze bioelectrical and audiosignals of a user with a biomonitoring neuroheadset.

4. System.

As shown in FIGS. 4A-4B, an embodiment of a system 200 for detectingbioelectrical signals and audio signals from a user includes: one ormore bioelectrical signal sensors 210 configured to collectbioelectrical signal data from the user; a noise reduction subsystem 220including one or more reference sensors configured to collect referencesignal data contemporaneously with the collection of bioelectricalsignal data; an auxiliary sensor (e.g., microphone(s), touch input(s),camera(s), kinematic sensor(s), and/or any other suitable sensor) 230configured to collect an auxiliary signal dataset (e.g., an audio signaldataset, a visual dataset) from the user contemporaneously with thecollection of the first bioelectrical signal dataset and the referencesignal dataset; a wearable support frame 240 worn at a head region ofthe user, the wearable support frame 240 supporting and physicallyconnecting the one or more bioelectrical sensors 210 and the one or morereference sensors; and an electronics subsystem 250 including aprocessing module 252 configured to produce a noise-reducedbioelectrical dataset from processing the bioelectrical signal data withthe reference signal data, and to produce a conditioned audio signaldataset from processing the audio signal dataset for transmission withthe noise-reduced bioelectrical signal dataset, the electronicssubsystem 250 electronically connected to the one or more bioelectricalsignal sensors 210, the noise reduction subsystem 220, and themicrophone 230.

In some variations, the system 200 can additionally or alternativelyinclude a communications module 254, a speaker 260, a remote processingmodule 270, and/or a screen (e.g., a sleeve, an electromagnetic shield,an insulator layer, etc.) separating bioelectrical sensor power wire(s)from microphone power wire(s) to facilitate prevention of cross-talkbetween corresponding signal data. As shown in FIGS. 6-10 , in specificexamples of configurations of the system 200, components of thebiomonitoring neuroheadset and/or other devices can communicate amongsteach other and/or the user.

In some variations, the system 200 can include multiple instances of thevarious components arranged in a bilateral configuration. In suchvariations, embodiments of the system 200 can be worn in a similarmanner as a pair of headphones having a connection (e.g., a frame orother support structure) that connects the bilaterally-arrangedcomponents (e.g., behind the head, beneath the chin, etc.). For example,as shown in FIGS. 13A-13C, the system can include a pair ofsubstantially mirror-imaged modules that are connected by a semi-rigidsupport structure, wherein each module includes an instance of thebioelectrical sensor 210, the processing unit 250, and the referencesensor(s) 220. Additionally or alternatively, the modules can beconnected by flexible wire, and/or be connected wirelessly. In thelatter example, each module can collect biosignals independently, anddistribute (e.g., to other modules of the system) and collect audiosignals (e.g. music, microphone signals) in a synchronized manner.

In some variations, the system 200 and/or components of the system 200can additionally or alternatively include or communicate data to and/orfrom: a user database (storing user account information, user profiles,user health records, user demographic information, associated userdevices, user preferences, etc.), an analysis database (storingcomputational models, collected datasets, historical data, public data,simulated data, generated data, generated analyses, diagnostic results,therapy recommendations, etc.), and/or any other suitable computingsystem.

Examples of system form factors include: headphones, ear buds, eyeglasses, helmets, caps, and/or any other suitable form factor.

Database(s) and/or portions of the method 100 can be entirely orpartially executed, run, hosted, or otherwise performed by: a remotecomputing system (e.g., a server, at least one networked computingsystem, stateless computing system, stateful computing system, etc.), abiomonitoring neuroheadset (e.g., a processing module 252 of abiomonitoring neuroheadset), a user device, a machine configured toreceive a computer-readable medium storing computer-readableinstructions, or by any other suitable computing system possessing anysuitable component (e.g., a graphics processing unit, a communicationsmodule 254, etc.). As shown in FIG. 6 , in specific examples, the system200 can include a remote processing module 270 remote from the one ormore bioelectrical signal sensor 210, the noise reduction subsystem, themicrophone, the wearable support frame, and the electronics subsystem250. In these specific examples, the remote processing module 270 can beconfigured to identify a blood flow time-varying oscillation innoise-reduced values of a noise-reduced EEG dataset; and estimate atleast one of a heart rate and a heart rate variability based on theblood flow time-varying oscillation in noise-reduced values. In theseexamples and related examples, the remote processing module 270 can beconfigured to separate cardiac data (e.g., heartbeat data, pulse data,etc.) embedded in EEG data from EEG signals originating from neuralactivation, and/or otherwise suitably determine cardiac data (e.g., viaECG monitoring, pulse oximetry, etc.). Additionally or alternatively,the remote processing module 270 can be configured to perform anysuitable portion of the method 100. However, the components of thesystem 200 can be distributed across machine and cloud-based computingsystems in any other suitable manner.

Devices implementing at least a portion of the method 100 can includeone or more of: a biomonitoring neuroheadset, smartwatch, smartphone, awearable computing device (e.g., head-mounted wearable computingdevice), tablet, desktop, a supplemental biosignal detector, asupplemental sensor (e.g., motion sensors, magnetometers, audio sensors,video sensors, location sensors a motion sensor, a light sensor, etc.),a medical device, and/or any other suitable device. All or portions ofthe method 100 can be performed by one or more of: a native application,web application, firmware on the device, plug-in, and any other suitablesoftware executing on a device. Device components used with the method100 can include an input (e.g., keyboard, touchscreen, etc.), an output(e.g., a display), a processor, a transceiver, and/or any other suitablecomponent, wherein data from the input device(s) and/or output device(s)can be generated, analyzed, and/or transmitted to entities forconsumption (e.g., for a user to assess their bioparameters)Communication between devices and/or databases can include wirelesscommunication (e.g., WiFi, Bluetooth, radiofrequency, etc.) and/or wiredcommunication. As shown in FIGS. 6-7 , in variations, communication canbe between an electronics subsystem 250 of a biomonitoring neuroheadsetand a computing device executing a software component. In variationsincluding wired communication between components of the system 200, thesystem 200 can additionally or alternatively include a screen (e.g.,separating power wires in a cable) configured to prevent cross-talkbetween collected signals. In a specific example, the system 200 caninclude: a cable connecting the processing module 252 to one or more EEGsensors 210 and the microphone; an EEG sensor power wire for the one ormore EEG sensors, the EEG sensor power wire positioned within theconnecting cable; a microphone power wire for the microphone, themicrophone power wire positioned within the connecting cable; and ascreen separating the EEG sensor power wire from the microphone powerwire within the connecting cable, the screen configured to facilitateprevention of cross-talk between the first EEG signal dataset and theaudio signal dataset. However, communication between components of thesystem and/or other devices can be configured in any suitable manner.

Components of the system 200 (e.g., a processing module 252 of anultrasound system) and/or any other suitable component of the system200, and/or any suitable portion of the method 100 can employ machinelearning approaches including any one or more of: supervised learning(e.g., using logistic regression, using back propagation neuralnetworks, using random forests, decision trees, etc.), unsupervisedlearning (e.g., using an Apriori algorithm, using K-means clustering),semi-supervised learning, reinforcement learning (e.g., using aQ-learning algorithm, using temporal difference learning), and any othersuitable learning style. Each processing portion of the method 100 canleverage: regression, classification, neural networks (e.g., CNNs, DNNs,etc.), sensor fusion, rules, heuristics, equations (e.g., weightedequations, etc.), selection (e.g., from a library), instance-basedmethods (e.g., nearest neighbor), regularization methods (e.g., ridgeregression), decision trees, Bayesian methods (e.g., Naïve Bayes,Markov), kernel methods, probability, deterministics, genetic programs,support vectors, or any, or any other suitable module leveraging anyother suitable computation method, machine learning method, orcombination thereof.

One or more bioparameter models can be concurrently generated, updated,or otherwise created. When the system includes multiple candidatebioparameter models (e.g., for a given bioparameter, for a set ofbioparameters, etc.), one or more bioparameter models can be selectedfrom the candidate bioparameter model set for use. The candidatebioparameter model can be selected based on: a test data set (e.g., ofbioelectrical data and/or supplemental data, associated with a knownbioparameter), consensus between the candidate bioparmaeter models, anyone element or any combination of elements (eg. groups of feature sets)derived from EEG, ECG, EOG, EMG, GSR, motion, GPS, audio and voicesignals, sentiment analysis, image and video analysis or any othercontemporaneous or contextual information, or otherwise determined.

4.1 Bioelectrical Signal Sensor.

The system 200 can include one or more bioelectrical signal sensors 210,which function to collect bioelectrical signal data from the user. Theone or more bioelectrical signal sensor 210 can include one or morebioelectrical signal sensor 210 configured to detect any one or more of:EEG signals, EOG signals, EMG signals, ECG signals, GSR signals, MEGsignals, and/or any other suitable signals. Bioelectrical signals can becollected by the one or more bioelectrical signal sensor 210 at anysuitable time period. For example, a set of EEG sensors 210 can collectEEG signal datasets contemporaneously during a time period with a set ofECG sensors collecting ECG signal datasets. However, the one or morebioelectrical signal sensor 210 can be configured to collect anysuitable signal at any suitable time.

The one or more bioelectrical signal sensor 210 are preferablypositioned proximal an ear canal region (e.g., left ear canal, right earcanal) of an ear region of the user, but can additionally oralternatively be positioned at, proximal to, adjacent to, near,distance, and/or with any suitable positional relationship to anysuitable ear subregion of an ear region of the user, and/or any suitableanatomical location of the user. In an example, one or morebioelectrical signal sensor 210 can be embedded within in-ear headphones(e.g., used for emitting audio) or on-ear headphones of a biomonitoringneuroheadset. However, bioelectrical signal sensor 210 can be positionedat any suitable location of a biomonitoring neuroheadset.

As shown in FIG. 6 , in a variation of the one or more bioelectricalsignal sensors 210, the system 200 can include a plurality of EEGsensors 210′. For example, the system 200 can include a first EEG sensor210′ positioned proximal an ear canal (e.g., left ear canal) of theuser, the first EEG sensor 210′ configured to collect a first EEG signaldataset from the user during a time period; and a second EEG sensor 210″positioned proximal a contralateral ear canal (e.g., a right ear canal)of the user, the second EEG sensor 210″ configured to collect a secondEEG dataset from the user during the time period. In another example,the first EEG sensor is positioned proximal an elastic cartilage sectionof the ear canal, and the second EEG sensor is positioned proximal anelastic cartilage section of the contralateral ear canal. Additionallyor alternatively, EEG sensors can be positioned on an external ear flap,in the cartilaginous chamber around the ear canal, or within the earcanal. Additionally or alternatively, one or more EEG sensors can bepositioned within, proximal, touching, and/or adjacent to the middle earand/or inner ear of either the left and/or right ear region. However,the system 200 can include any suitable configuration of a set of EEGsensors 210.

Additionally or alternatively, bioelectrical signal sensor 210 caninclude any elements described in U.S. application Ser. No. 13/903,832,filed 28 May 2013, and U.S. patent application Ser. No. 14/447,326 filed30 Jul. 2014, which are each herein incorporated in their entirety bythis reference. However, bioelectrical signal sensors 210 can beconfigured in any suitable fashion.

4.2 Noise Reduction Module.

The system 200 can include a noise reduction module 220 including one ormore reference sensors. The noise reduction module 220 functions toemploy one or more reference sensors to collect reference signal datafor reducing noise associated with collected bioelectrical signaldatasets. A noise reduction module 220 preferably includes one or morecommon mode sensors 222, but can additionally or alternatively includeone or more DRL electrodes 224 and/or any other suitable referencesensors.

In relation to reference sensors of the reduction module 220, referencesensors are preferably positioned proximal an ear region of a user(e.g., a temporal bone of a user), but can additionally or alternativelybe positioned at, proximal to, adjacent with, and/or distant from anysuitable anatomical location of the user. In an example, the system 200can include a noise reduction subsystem including a reference sensorpositioned proximal a mastoid process of a temporal bone proximal theear canal, the reference sensor configured to collect a reference signaldataset contemporaneously with collection of an EEG signal dataset(e.g., by one or more bioelectrical signal sensor 210) during a firsttime period. In this example, the reference sensor can be a first commonmode sensor 222, where the reference signal dataset is a first commonmode signal dataset, where the noise reduction subsystem can furtherinclude a driven right leg module positioned proximal the first commonmode sensor 222 and the mastoid process of the temporal bone. In thisexample, the noise reduction subsystem can further include a secondcommon mode sensor 222 positioned proximal a contralateral mastoidprocess of a contralateral temporal bone proximal the contralateral earcanal, the second common mode sensor 222 configured to collect a secondcommon mode signal dataset. However, reference sensors of the noisereduction module 220 can be configured in any suitable manner.

In a variation of the noise reduction module 220, a set of referencesensors can include, for each ear, one or more common mode sensors 222and one or more DRL electrodes 224 positioned proximal the ear. In aspecific example, for a given ear of the user, a corresponding commonmode sensor 222 can be positioned proximal a different ear subregion(e.g., a different part of the ear flap) than the corresponding DRLelectrode 224. In another variation of the noise reduction module 220,only one type of reference sensor can correspond to an ear of the user.For example, a common mode sensor 222 can be characterized with aprimary reference location behind the left ear flap, and a driven rightleg sensor can be characterized with a feedback reference locationbehind a right ear flap. However, any suitable combination of referencesensor types can be arranged at any suitable ear region of a user.

Additionally or alternatively, the noise reduction module 220 caninclude any elements described in U.S. patent application Ser. No.14/447,326, filed 30 Jul. 2014, which is hereby incorporated in itsentirety by this reference. However, the noise reduction module 220 andthe one or more reference sensors can be configured in any suitablefashion.

4.3 Auxiliary Sensor.

The system 200 can include one or more auxiliary sensors 230, whichfunction to collect an auxiliary signal dataset from the user for use indetermining a psychological and/or physiological state of the user. Theone or more auxiliary sensors 230 are preferably microphones, but canadditionally or alternatively be any other suitable sensor. In caseswherein the auxiliary sensors 230 are microphones 230, the microphonescan be typified by one or more microphone types including dynamic,ribbon, carbon, piezoelectric, condenser, fiber optic, laser, liquid,microelectromechanical systems (MEMS), and/or any other suitablemicrophone type. The one or more microphones 230 can include anysuitable capsule (e.g., with respect to geometry, form, orientation,size, weight, color, materials, etc.) for housing the electricalcomponents of the microphone 230. As shown in FIG. 6 , in variationsexamples, the microphone 230 can be embedded with volume controls foradjusting volume of a speaker 260 of the biomonitoring neuroheadsetand/or other suitable component. As shown in FIG. 5B, the microphone 230can otherwise be omitted from the system 200.

The microphone 230 is preferably configured to collect one or more audiosignal datasets from the user contemporaneously with collection of oneor more bioelectrical signal datasets and/or reference signal datasets(e.g., during a time period). However, the microphone 230 can beconfigured to perform any suitable operation.

The microphone 230 is preferably in communication (e.g., wiredcommunication, wireless communication) with a processing module 252 ofan electronics subsystem 250 of the biomonitoring neuroheadset, but canadditionally or alternatively possess a communication link with anyother suitable component of the biomonitoring neuroheadset, the system200, and/or another device.

As shown in FIGS. 5A and 5C, the microphone 230 is preferably positionedproximal an oral cavity of the user, but can additionally oralternatively be positioned at, proximal to, adjacent to, near, far,and/or with any suitable positional relationship to any anatomicalposition of the user and/or component of the biomonitoring neuroheadset.

However, the microphone 230 can be configured in any suitable manner.

4.4 Wearable Support Frame.

The system 200 can include one or more wearable support frames 240,which function to provide support for components of the biomonitoringneuroheadset. The one or more wearable support frames 240 preferablysupport and/or physically connect one or more bioelectrical signalsensor 210 and/or one or more reference sensors. The wearable supportframe 240 can possess any suitable dimensions (e.g., width, length,height, surface area, volume, aspect ratio, curvature, etc.). Thewearable support frame 240 can include any suitable three-dimensionalshapes including: a prism, cube, cylinder, sphere, and/or any suitablethree-dimensional shape. The shape of a surface of the wearable supportframe 240 can include: a rectangle, square, circle, triangle, polygon,and/or other suitable shape. As shown in FIGS. 4A-4B, 5B-5C, and 6, awearable support frame 240 can include a primary curvature forming ahook configured to hook around an ear of a user, the ear regionsupporting the wearable support frame 240 on the user. However, one ormore wearable support frames 240 can possess any suitable form.

One or more wearable support frames 240 are preferably worn at a headregion, but can additionally or alternatively be worn at any suitableanatomical position (e.g., chest region, bones, forehead, etc.) forfacilitating mechanical retention of the biomonitoring device to theuser. The wearable support frame 240 is preferably mechanicallysupported at an ear region of the user. In a specific example, thesystem 200 can include a wearable support frame 240 worn at a headregion of the user and cooperatively supported at an ear region proximalthe temporal bone and an ear flap of the user, the wearable supportframe 240 supporting and physically connecting the EEG sensor 210 andthe reference sensor. However, one or more wearable support frames 240can be positioned at any suitable location and supported by any suitablebody region and/or component.

As shown in FIG. 6 , in a variation, the system 200 includes a pluralityof wearable support frames 240. For example, the system 200 can includea first wearable support frame 240′ cooperatively supported at an earregion, the first wearable support frame 240′ supporting and physicallyconnecting a first EEG sensor, a first common mode sensor 222′, and afirst DRL electrode 224′ of a DRL module; and a second wearable supportframe 240″ cooperatively supported at a contralateral ear region, thesecond wearable support frame 240″ supporting and physically connectinga second EEG sensor, a second common mode sensor 222″, and a second DRLelectrode 224″ of a DRL module. Alternatively, as shown in FIG. 12 , abiomonitoring neuroheadset can include only a single wearable supportframe configured to be worn at a single ear region (e.g., a left earregion or a right ear region) of a user.

However, the one or more wearable support frames 240 can be configuredin any suitable fashion.

4.5 Electronics Subsystem.

As shown in FIG. 4B, the system 200 can include an electronics subsystem250, which functions to receive, process, and/or transmit signalscollected by one or more bioelectrical signal sensor 210, referencesensors, and/or a microphone 230. The electronics subsystem 250 canadditionally or alternatively include a processing module 252 and/or acommunications module 254. However, the electronics subsystem 250 caninclude any other suitable modules configured to facilitate signalreception, signal processing, and/or data transfer in an efficientmanner.

The electronics subsystem 250 is preferably electronically connected tothe one or more bioelectrical signal sensors 210, the noise reductionsubsystem 220, and the microphone 230, but can additionally oralternatively be connected (e.g., wired connection, wireless connection)to any suitable component of the biomonitoring neuroheadset, and/or anysuitable component.

Components of the electronics subsystem 250 are preferably embeddedwithin one or more wearable support frames 240 of the biomonitoringneuroheadset, but can be otherwise located at the biomonitoringneuroheadset and/or other component.

However, the electronics subsystem 250 can be configured in any suitablemanner.

4.5.a Processing Module.

As shown in FIG. 4B, the electronics subsystem 250 can include aprocessing module 252 functioning to process collected and/or receiveddatasets. The processing module 252 can additionally or alternativelyfunction to control notifications to a user, generate a bioparameter,generate operation instructions for a user device, and/or perform anyother suitable operations related to the method 100. The processingmodule 252 can include one or more: microcontrollers, central processingunits (CPU), a microprocessors, digital signal processors (DSP), a statemachine, an application-specific integrated circuit (ASIC), aprogrammable logic device (PLD), a field programmable gate array (FPGA),a graphics processing unit (GPU), any other suitable processing device.The processing module 252 preferably includes one or more printedcircuit boards (PCBs), which can preferably satisfy the data collectionand/or processing requirements associated with the method 100. In aspecific example, the electronics subsystem 250 includes a control PCBembedded in a wearable support frame 240 configured to hook onto an earregion of the user. In another specific example, the electronicsubsystem includes a set of daughter PCBs, where at least one daughterPCB is embedded in each of a plurality of wearable support frames 240(e.g., a wearable support frame 240 for each ear). In another specificexample, the electronics submodule can include a first processingsubmodule (e.g., a control PCB, a daughter PCB, etc.) positionedproximal a first EEG sensor, and a second processing submodule (e.g., acontrol PCB, a daughter PCB, etc.) positioned proximal a second EEGsensor. However, the processing module 252 can include any suitablecomponents.

The processing module preferably includes an averaging circuit, whichfunctions to produce an averaged reference signal dataset fromprocessing multiple reference signals (e.g., CMS signals, DRL signals,etc.), such that bioelectrical signal datasets can be referenced againstthe averaged reference signal dataset. The averaging circuit can beimplemented as an analog averaging circuit in accordance with knownanalog signal processing methodologies, and/or as a digital averagingcircuit (e.g., within a microcontroller of the processing module). In aspecific example, the averaging circuit averages the first and secondreference signal datasets to generate an averaged reference signaldataset, and two or more bioelectrical signal datasets are eachreferenced against the averaged reference signal dataset (e.g., as adifferential voltage measurement). In another specific example, as shownin FIG. 10 , the averaging circuit combines the outputs of a pluralityof CMS sensor modules to generate the averaged reference signal dataset,which is subsequently passed into an amplifier for downstreamprocessing. However, the averaging circuit can additionally oralternatively include any other suitable components, and/or be otherwisesuitably implemented to generate the averaged reference signal dataset.

The averaging circuit can be implemented as a fully analog circuit, afully digital circuit, fully in software, and/or any suitablecombination of the aforementioned.

The processing module 252 is preferably configured to produce anoise-reduced EEG dataset from processing one or more EEG signaldatasets with one or more reference signal datasets, and to produce aconditioned audio signal dataset from processing one or more audiosignal datasets for transmission with the noise-reduced EEG dataset. Theprocessing module 252 can be additionally or alternatively configured tocontrol data collection parameters for collection of bioelectricalsignal datasets, reference signal datasets, and/or audio signaldatasets. Data collection parameters can include: sampling frequency,time of sampling, time between samples, amount of data to collect, typesof data to collect, conditions for triggering collection, voltageresolution, voltage amplitude, signal dynamic range, and/or any suitabledata collection parameter. However, the processing module 252 can beconfigured to perform any suitable portion of the method 100.

A specific example implementation of the signal processing architecture,as shown in FIG. 14 , can include: receiving new data (e.g., EEG signaldata, ECG data extracted from EEG signal data, ECG data gathereddirectly from an ECG sensor, any other suitable time-varying signaldata, etc.); removing a moving average of the new data; calculating theroot mean squared (RMS) and maximum swing (e.g., peak-to-peak amplitude)of the new data; determining that the RMS and/or maximum swing valuesexceed a threshold; in response to the RMS and/or maximum swing valuesexceeding the threshold, transforming the new data to a zero value;concatenating the new data to a data buffer; applying a first transform(e.g., a discrete wavelet transform) to the data buffer to generatetransformed data; thresholding the coefficients of the transformed data(e.g., setting coefficients of the transformed data that fall below athreshold to a zero value); applying an inverse transform (e.g., aninverse discrete wavelet transform) to generate filtered data;determining if the sign of the peak is locked; in response todetermining that the peak sign is locked, extracting peak data into apeak vector; selecting peaks from the peak vector that are within a lookback period; determining that selected peaks within the look back periodare valid according to a set of validity criteria (e.g., within a validamplitude range, within a valid frequency range, within the look backtemporal range, etc.); setting the state of the system to a lockingstate, wherein the processing module is synchronized to the signalsencoded by the received new data, based on the peak-to-peak distance(e.g., whether the peak-to-peak distance is greater than an R-R valuemoving average, a maximum threshold, etc.).

The above and related specific implementations can additionally oralternatively include, as shown in FIG. 14 , in response to determiningthat the peak sign is not locked, counting positive and negativepolarity peaks in the filtered data that exceed a threshold amplitude;determining whether there are a greater number of positive peaks ornegative peaks, and determining that the signal is negative peak lockedif there is a greater number of negative peaks and determining that thesignal is positive peak locked if there is a greater number of positivepeaks; in response to determining that there are no valid peaksaccording to the set of validity criteria, setting the state of theprocessing module to a waiting state and ending the processing loop;based on the peak-to-peak distance, subtracting a differential value(e.g., a R-R distance moving average value, a threshold value, etc.)from the peak vector to generate a differential peak vector; selecting alowest differential peak value (e.g., having the highest expected valueof validity, a largest expected value of recurrence, etc.) anddetermining that the selected lowest differential peak value added to aportion (e.g., 80%, one half, etc.) of an average value (e.g., an R-Rdistance moving average value), which together constitute a test value,is greater than a length of the peak data; eliminating inconsistentpeaks based on the comparison between the length of the peak data andthe test value to generate a modified differential peak vector; andupdating the average value (e.g., the R-R moving average value) based onthe modified differential peak vector.

The processing module can additionally or alternatively execute or beconfigured to: prefilter the data to remove offsets and signal drift,detect artifacts (eg. eye blinks, skin movements, muscle signals),classify and reverse these artifacts (eg. by independent componentsanalysis), map surface signals to modelled internal sources (eg. bylow-resolution tomography), quantify the strength and topography ofnetwork connectivity (eg, Granger Causality), measure nonlinearity orchaotic features (eg. Lyupanov dimensionality), measure frequencycomponents (eg. Fourier Transform, time-frequency methods etc), orperform any other suitable EEG signal processing method to extractrelevant features for development or evaluation of specific bioparametermodels.

Additionally or alternatively, the processing module 252 can beconfigured in any suitable manner.

4.5.B Communications Module.

As shown in FIG. 4B, the electronics subsystem 250 can include acommunications module 254 functioning to receive and/or transmit data(e.g., a bioparameter, a cognitive state metric, a bioelectrical signaldataset, a combined audio signal and bioelectrical signal dataset, etc.)with a remote server, a user device, and/or any suitable component. Thecommunication submodule preferably includes a transmitter, and canadditionally or alternatively include a receiver. In a variation, thecommunication module can facilitate wired communication between thebiomonitoring neuroheadset and another device (e.g., a smartphone of auser). In this variation, wired communication can be through a cablewith connectivity for an audio jack, USB, mini-USB, lightning cable,and/or any suitable wired connection medium. In another variation, thecommunication module can facilitate wireless communication between thebiomonitoring neuroheadset and another device. Wireless communicationcan be facilitated through Zigbee, Z-wave, WiFi, but can additionally oralternatively be facilitated through short-range wireless communicationincluding Bluetooth, BLE beacon, RF, IR, or any other suitable wirelesscommunication medium.

The communications module 254 is preferably configured to transmit anysuitable dataset (e.g., a bioelectrical signal dataset, an audio signaldataset, a combined bioelectrical signal and audio signal dataset, etc.)to any suitable device. In a specific example, the communications module254 can be configured to transmit the noise-reduced EEG dataset to themobile computing device for transmission to a remote processing module270, and/or to transmit the audio signal dataset to a mobile computingdevice of the user, wherein the audio signal dataset specifiesinstructions for controlling operation of the mobile computing device.However, the communications module 254 can be configured to perform anysuitable operation.

The communication submodule can additionally or alternatively include arouter (e.g., a WiFi router), an extender for one or more communicationprotocols, a communication protocol translator, or include any othersuitable communication submodule. The communication submodule can alsoadditionally or alternatively include or be communicatively coupled toRAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), harddrives, floppy drives, and/or any suitable data storage device

However, the communication submodule can receive, convert, and/ortransmit any type of suitable signal or data to any suitable componentor device.

4.6 Speaker.

The system 200 can include one or more speaker 260 of a biomonitoringneuroheadset. The speaker 260 functions to output audio. Output audiocan be used to promote a therapy and/or facilitate physiological and/orpsychological monitoring (e.g., a user response to the outputted audio)of the user. Output audio can additionally or alternatively be used toprovide desired audio output to the user (e.g., play a song, act as ahands-free headset for a phone, etc.). Output audio can, however, haveany other suitable purpose. The speaker 260 is preferably embedded witha biomonitoring neuroheadset. In particular, the speaker 260 ispreferably embedded in an ear bud physically supported by the wearablesupport frame 240 and additionally housing portions one or morebioelectrical signal sensor 210. In a specific example, the system 200can include a speaker 260 positioned proximal an EEG sensor 210 and anear canal of the user. Additionally or alternatively, one or morespeakers 260 can be positioned at any location in relation to componentsof the biomonitoring neuroheadset. However, the speaker 260 can beremote from the biomonitoring neuroheadset (e.g., a speaker 260wirelessly communicating with the biomonitoring neuroheadset), or canotherwise be omitted from the system 200.

The speaker 260 is preferably controlled by the processing module 252 ofthe electronics subsystem 250, but can additionally or alternatively becontrolled by any suitable component. The speaker 260 can be configuredto emit audio samples generated by and/or transmitted by any suitableuser device. In a specific example, the communications module 254 of theelectronics subsystem 250 can be configured to receive from a mobilecomputing device an audio sample transmitted based on instructionsextracted from a collected audio signal input of the user, where thespeaker 260 can be configured to emit the audio sample.

In a variation, the one or more speakers 260 can be configured to emitone or more audio samples from which a user response can be measured(e.g., by a bioelectrical signal sensor 210, by a microphone 230). In aspecific example, one or more EEG sensors 210 can be configured tocollect a second EEG signal dataset (e.g., where a first EEG signaldataset was collected prior to emission of the audio sample) from theuser during a time period in response to emission of the audio sample,and wherein one or more reference sensors are further configured tocollect a second reference signal dataset (e.g., where the firstreference signal dataset was collected prior to emission of the audiosample) from the user contemporaneously with collection of the secondEEG signal dataset during the time period. In another specific example,repetitive stimuli are applied to the user (e.g., a beep or othersuitable audible stimulus of 0.25 s duration at Boo Hz, repeated at 1.2sec intervals) and the response is averaged over equivalent portions ofthe response relative to the onset of the stimulus (e.g., in atime-locked manner), to derive an evoked response signal (e.g., abioelectrical signal dataset corresponding to an evoked response). In arelated example, a proportion of the supplied audible stimuli have adifferentiating property (e.g., a different pitch, such as 1100 Hz), andare used to generate a differential evoked response signal (e.g.,averaged across the equivalent portions of the associated response tothe stimuli having the differentiating property) separate from theevoked response signal corresponding to the audible stimuli not havingthe differentiating property (e.g., at the pitch of Boo Hz). Thedifferential evoked response and the evoked response can be compared,and the comparison can be used to derive information about the user(e.g., cognitive processes utilized by the user, cognitive decline,fatigue, hearing acuity, etc.).

However, one or more speakers 260 can be configured in any suitablemanner.

4.7 Supplemental Sensors.

The system 200 can include one or more supplemental sensors, whichfunction to collect supplemental data to aid in monitoring psychological(e.g., cognitive state metric) and/or physiological status (e.g.,cardiovascular parameters, health status parameter, etc.) of a user. Oneor more supplemental sensors can include: motion sensors (e.g.,accelerometers, gyroscopes), magnetometers, audio sensors, videosensors, location sensors, and/or any other suitable sensor. Asupplemental sensor is preferably arranged at a suitable location of thebiomonitoring neuroheadset, but can additionally or alternatively bepositioned at any suitable location (e.g., as part of user devicedistinct from the biomonitoring neuroheadset, etc.). However, one ormore supplemental sensors can be configured in any suitable manner.

In an example, one or more supplemental sensors can include a motionsensor configured to detect one or more user motion features indicatinga user's gait, imbalance, tremors, exercise habits, counting steps,movement restrictions, and/or any other suitable user motion feature.

In another example, the supplemental sensors can include one or morecameras. The cameras can be mounted to the system (e.g., be pointed atthe user, pointed at an external environment, etc.), mounted distal thesystem, or be otherwise arranged. The cameras can be stereoscopic,visible spectrum, invisible spectrum (e.g., IR), or be otherwiseconfigured. In a specific example, images and/or video captured by thecameras can provide a feed of the current use environment and/or usermotion (e.g., using optical flow, etc.), which can then be mappedagainst the EEG, audio and other bioparameters to provide contextualinformation and real time feedback that is dynamically suited to thesituation.

User motion features and/or any suitable data collected by one or moresupplemental sensors can be used with, combined with, and/or processedin any suitable manner with collected bioelectrical signal data and/orother suitable datasets in order to determine a bioparameter (e.g.,cognitive state metric, cardiovascular parameter such as heart beat,etc.), control operation of a user device (e.g., detecting user gesturalinstructions such as nodding, head shaking, control taps on casing, headmotion, body motion, facial expressions, etc.), and/or perform any othersuitable operation in relation to the method 100. The supplemental datastreams can additionally or alternatively be used to: verify thedetermined bioparameter, determine the bioparameter (e.g., a differentversion of the same bioparameter, using a different set of inputs and/ormethodologies, wherein the best bioparameter value, such as thebioparameter value with the highest confidence level, can be selectedfrom the set of potential bioparameter values), determine a backupbioparameter (e.g., when the primary data set, such as the EEG signals,are unavailable or have noise above a noise threshold), triggersubsequent analyses (e.g., trigger EEG signal sampling, triggerbioparameter verification or validation), or be used in any othersutiable manner. However, one or more supplemental sensors can beconfigured to perform any suitable operation.

The method 100 and/or system 200 of the embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor, though any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

The FIGURES illustrate the architecture, functionality and operation ofpossible implementations of systems, methods and computer programproducts according to preferred embodiments, example configurations, andvariations thereof. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, step, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block can occurout of the order noted in the FIGURES. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changes(e.g., combinations of any suitable number of any of the variants andembodiments disclosed above) can be made to the embodiments of theinvention without departing from the scope of this invention as definedin the following claims.

We claim:
 1. An electroencephalogram (EEG) headset, comprising: a firstand second earpiece, each earpiece comprising: an in-ear earbud,comprising a speaker and an EEG sensor; and an ear hook mounted to theearbud, comprising a common mode sensor vertically arranged relative toa driven right leg electrode, wherein the ear hook is configured to biasboth the common mode sensor and the driven right leg electrode againstskin of a mastoid process of a user; and a processing system connectedto the first and second earpiece, the processing system configured to:generate a virtual reference signal, corresponding to a virtualreference location, from signals received from the common mode sensors;determine a cancellation signal to apply to each driven right legelectrode based on the virtual reference signal; and determine abioparameter for the user based on an EEG signal dataset received fromat least one of the EEG sensors of the first or second earpieces.
 2. TheEEG headset of claim 1, wherein at least one of the first or secondearpieces further comprises an accelerometer.
 3. The EEG headset ofclaim 1, wherein at least one of the first or second earpieces furthercomprises a microphone.
 4. The EEG headset of claim 1, wherein the EEGsignal dataset is referenced to the virtual reference signal
 5. The EEGheadset of claim 1, wherein, for each ear piece, the ear hook comprises:a primary curvature forming a hook, wherein the common mode sensor andthe driven right leg electrode each protrude from the primary curvature.6. The EEG headset of claim 1, wherein, for each earpiece, the commonmode sensor and the driven right leg electrode are configured to bepositioned behind an ear flap of the user.
 7. The EEG headset of claim1, wherein the processing system comprises: a first processing subsystemconnected to the first earbud; and a second processing subsystem mountedto the second earbud.
 8. The EEG headset of claim 7, wherein at leastone of the first or second processing subsystems comprises an averagingcircuit configured generate the reference signal dataset from thesignals received from the first and second reference sensors in realtime with receiving the signals.
 9. The EEG headset of claim 8, whereinthe averaging circuit is configured to perform a weighted average of thesignals to generate the reference signal dataset.
 10. The EEG headset ofclaim 1, wherein the virtual reference location is located between thefirst reference signal dataset and the second reference signal datasetin signal space.
 11. A system, comprising: an earbud mounted to a firstear hook, the earbud comprising an EEG sensor configured to bepositioned proximal to an ear canal of an ear of a user, the EEG sensorconfigured to collect an EEG signal dataset from the user; the first earhook supported at the ear of the user and comprising a first referencesensor, the first ear hook physically connecting the EEG sensor and thefirst reference sensor, the first ear hook configured to position thefirst reference sensor behind a flap of the ear of the user and incontact with skin on a mastoid process of a temporal bone; a second earhook supported at a contralateral ear of the user and comprising asecond reference sensor, the second ear hook configured to position thesecond reference sensor configured to be positioned behind a flap of thecontralateral ear of the user and in contact with skin on acontralateral mastoid process of a contralateral temporal bone; and aprocessing module comprising an electronics subsystem mounted to theearbud and electronically connected to the EEG sensor, the processingmodule configured to determine a bioparameter based on the EEG signaldataset, wherein the EEG signal dataset is referenced to a virtualreference signal generated based on reference signals received from thefirst and second reference sensors.
 12. The system of claim 1, furthercomprising a second earbud mounted to the second ear hook, the secondearbud comprising a second EEG sensor configured to be positionedproximal to an ear canal of the contralateral ear of a user, the secondEEG sensor configured to collect a second EEG signal dataset from theuser, wherein the processing module is configured to further determinethe bioparameter based on the second EEG signal dataset.
 13. The systemof claim 12, wherein the second EEG signal dataset is referenced to asecond virtual reference signal generated based on reference signalsreceived form the first and second reference sensors.
 14. The system ofclaim 12, wherein the processing module is configured to produce anaggregated EEG dataset based on the EEG signal dataset and the secondEEG signal dataset, wherein the processing module is configured todetermine the bioparameter based on: the EEG signal dataset, the secondEEG signal dataset, and the aggregated EEG dataset.
 15. The system ofclaim 1, wherein the first reference sensor is a first common modesensor, wherein the first ear hook comprises a driven right leg (DRL)electrode, wherein the first ear hook is further configured position theDRL electrode behind the flap of the ear of the user and in contact withskin on the mastoid process of the temporal bone, wherein the DRLelectrode outputs a driven right leg signal based on the virtualreference signal.
 16. The system of claim 15, wherein the first ear hookis configured to position the first reference sensor within three inchesof the EEG sensor and the DRL module.
 17. The system of claim 15,wherein the first ear hook is configured to position the DRL electrodesubstantially vertically aligned relative to the first reference sensor.18. The system of claim 15, wherein the EEG sensor is configured to bepositioned substantially laterally aligned relative to at least one ofthe first reference sensor or the driven right leg electrode.
 19. Thesystem of claim 1, wherein the bioparameter comprises a cardiovascularparameter, wherein the processing module is further configured togenerate a cognitive state metric for the user based on the EEG signaldataset and the cardiovascular parameter.
 20. The system of claim 18,wherein the processing module is further configured to: identify atime-varying oscillation in values of the EEG signal dataset; andestimate the cardiovascular parameter based on the time-varyingoscillation in values.